J Neurol DOI 10.1007/s00415-015-7711-x
Recent imaging advances in neurology Lorenzo Rocchi1 • Flavia Niccolini1 • Marios Politis1
Received: 2 December 2014 / Revised: 13 March 2015 / Accepted: 14 March 2015 Ó Springer-Verlag Berlin Heidelberg 2015
Abstract Over the recent years, the application of neuroimaging techniques such as magnetic resonance imaging (MRI) and positron emission tomography (PET) has considerably advanced the understanding of complex neurological disorders. PET is a powerful molecular imaging tool, which investigates the distribution and binding of radiochemicals attached to biologically relevant molecules; as such, this technique is able to give information on biochemistry and metabolism of the brain in health and disease. MRI uses high intensity magnetic fields and radiofrequency pulses to provide structural and functional information on tissues and organs in intact or diseased individuals, including the evaluation of white matter integrity, grey matter thickness and brain perfusion. The aim of this article is to review the most recent advances in neuroimaging research in common neurological disorders such as movement disorders, dementia, epilepsy, traumatic brain injury and multiple sclerosis, and to evaluate their contribution in the diagnosis and management of patients. Keywords Magnetic resonance imaging Positron emission tomography Movement disorders Dementia Epilepsy Traumatic brain injury Multiple sclerosis
& Marios Politis [email protected]
Neurodegeneration Imaging Group, Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London SE5 8AF, UK
Introduction Several neuroimaging techniques have been deployed over the past decade, to help understand the pathophysiology of complex neurological disorders and to provide us with biological indicators for early diagnosis and response to treatment [1, 2]. Neuroimaging techniques such as magnetic resonance imaging (MRI) and functional MRI (fMRI) have played a critical role in assessing structural and functional changes in the brain, which can be related to the pathophysiology and clinical manifestations of neurological disorders (Table 1). Positron emission tomography (PET) molecular imaging, by measuring the distribution of a radionuclide that is introduced into the body on a biologically active molecule, is a powerful technique for the investigation of in vivo metabolic and biochemical abnormalities in the brain (Table 2). In this article, we review recent neuroimaging advances in common neurological disorders.
Movement disorders Parkinson’s disease (PD) is the second most common neurodegenerative disease after Alzheimer’s disease (AD), with a life-time risk of 4 % that is predicted to double over the next decades. Although neuroimaging in PD has provided us with important information relating to changes in brain structure and function, metabolic activity and brain neurochemistry, no neuroimaging modalities are specifically recommended for routine use in clinical practice . Despite this, PET studies in PD have significantly contributed to the understanding of the underlying pathology, such as alterations in the dopaminergic network .
J Neurol Table 1 Positron emission tomography techniques in neurologic disorders
Presynaptic dopaminergic function
Presynaptic serotonergic function
Postsynaptic dopaminergic receptors/dopamine release
Presynaptic dopaminergic function
Presynaptic dopaminergic function
Neuroinflammation Ab-amyloid deposits
Tau protein deposits
Table 2 Magnetic resonance imaging techniques in neurological disorders MRI technique
Use in neurological disorders
Arterial spin labeling Blood oxygenation level-dependent
Magnetically labeled water in the blood stream Difference in hemoglobin saturation
Brain perfusion Estimation of brain activity
Diffusion tensor imaging
Water molecules diffusion in white matter
Characterization of white matter
Degree of diffusion anisotropy in white matter
Estimation of white matter damage
Effect of exogenous vibration
Elasticity of brain parenchyma
Magnetization transfer saturation
Pulsed applied at lower frequency
Marker of structural integrity
Diffusion of water molecules irrespective of direction
Estimation of white matter damage
Neuromelanin sensitive MRI
Pulsed applied at lower frequency
Quantification on neuromelanin deposits
Resting state fMRI
Difference in hemoglobin saturation
Estimation of brain activity in resting conditions
Transverse relaxation rate
Relaxation of transverse magnetization
Estimation of iron deposits
Recent studies have shown that novel neuroimaging tools could aid in the early diagnosis and monitoring of PD such as [18F]DTBZ PET, a vesicular monoamine transporter 2 (VMAT-2) marker, for measuring dopaminergic terminal loss [5, 6]. Semi-automated measurement of substantia nigra pars compacta (SNc) volume with 3Dneuromelanin sensitive MRI has been demonstrated to be able to distinguish PD patients from healthy subjects (HS), especially in the early stage of the disease . Ohtsuka and
coworkers  showed that the volume of the SNc and locus coeruleus (LC) as measured with neuromelanin sensitive MRI was able to distinguish early PD patients from those with multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). PD patients could also be distinguished from HS based on lower magnetization transfer values of the SNc, which represent a marker of structural integrity . Iron deposition measured with transverse relaxation rate (R2*) in the caudal putamen and substantia
nigra has been suggested as a biomarker of disease progression in PD . Although the number of patients studied in these reports is limited, quantification of iron and neuromelanin deposition seems to be promising tools for the early diagnosis in PD. Arterial spin labeling (ASL) also appears to be a promising addition in the early diagnosis of PD due to its ability to identify a perfusion covariance pattern, specific for patients with PD, which is consistent with decreased perfusion bilaterally in the temporal, insular, posterior parietal, inferior parietal, lateral occipital and prefrontal cortices, and with relative perfusion increases in the cerebellum, pons, right thalamus, pallidum, sensorimotor cortex, paracentral lobule and supplementary motor area . Diffusion tensor imaging (DTI) has demonstrated a number of white matter abnormalities in PD, such as an increase in mean diffusivity (MD) and a decrease in fractional anisotropy (FA) in the SNc and olfactory tract . Other DTI measures such as radial and axial diffusion have shown changes in PD in the basal ganglia and thalamus . A novel diffusion MRI technique, namely track density imaging, has shown signal changes in areas affected by PD pathology . Although promising, systematic analysis of the contribution of DTI measures as an aid to PD diagnosis is yet to be made. Transcranial sonography (TCS) is a non-invasive technique that has been evaluated as a diagnostic tool in PD. Although echogenicity of the SN has been considered useful to aid PD diagnosis by several studies [15, 16], more recent literature suggests that TCS is less sensitive in detecting early stage PD compared to DAT PET with [11C]CFT , and it cannot be recommended for routine clinical use . Nigral lesion load quantification by TCS, which evaluates echogenicity by summing four values, two on each side of SN, could be a more accurate marker for PD diagnosis ; however, that needs to be explored further in future studies. Cognitive impairment is one of the most common and important non-motor aspects of PD . PET with [11C]PiB has shown that Ab-amyloid deposition in PD patients at risk for dementia is uncommon , and it cannot differentiate PD patients with mild cognitive impairment (MCI) from those without. A study indicated that [11C]PiB PET could be useful only in predicting decline in executive functions in PD . Loss of dopaminergic terminals measured with [18F]FP-CIT, a radioligand tagging DAT, correlates with a glucose specific cognition-related disease network evaluated by [18F]FDG PET. This pattern shows a reduction of metabolic activity in frontal and parietal regions with increases in the cerebellar vermis and dentate nuclei in cognitively impaired PD patients . Inflammation quantified with [11C]PK11195 PET, which evaluates the activity of mitochondrial translocator protein
(TSPO), a marker of microglial activation [24–26], was shown to correlate with mini mental examination scores [27, 28] and with reduced glucose metabolism measured by [18F]FDG PET . Overall, these data suggest a role of neuroinflammation in the development of dementia in PD (PDD), but the clinical impact of a possible anti-inflammatory therapy has not been established yet. A number of morphological brain measures have been used to investigate the pathophysiology of cognitive decline in PD. Cortical thinning, which has been suggested to be linked to overall cognitive performance , and to specific cognitive domains [30, 31], may be useful in identifying PDD , even at early stages . Cerebral white matter atrophy and volumes of the lateral ventricles and hippocampi have been related to dementia in PD . A faster rate of thinning in various cortical regions has been associated with the early presence of MCI in PD , and it has been suggested that hippocampal volume is a major factor in predicting the development of mild cognitive impairment and dementia in PD . Besides cortical alterations, white matter deterioration, and in particular decreases in FA occurring in the left anterior cingulated bundle, corpus callosum splenium  or the frontal and interhemispheric connections  may underlie progressive cognitive impairment in PD. Impulse control disorders (ICDs) following dopamine replacement therapy represent a significant problem for PD patients and their carers. Using serial [11C]raclopride PET (a specific D2 receptor radioligand) scanning, it was found that patients with ICDs had a greater ventral striatal dopamine release following exposure to reward related cues compared to PD patients without ICDs . One study with fMRI has demonstrated that PD patients with hypersexuality, when exposed to sexual cues, had greater blood oxygen level-dependent (BOLD) signal changes in regions within limbic, paralimbic, temporal, occipital, somatosensory and prefrontal cortices. These changes were present only when patients were ON medication, indicating that dopamine therapy may release inhibition within specific circuits linked to compulsive sexual behaviour . PET and MRI have been used to understand the mechanisms underlying motor signs of PD, which have proven challenging to manage in clinic. Gait disorder is one of the most disabling and treatment resistant symptoms of PD, and has recently been linked to cortical cholinergic denervation , in line with the hypothesis that the condition has a largely non-dopaminergic basis . In addition, PD gait disorder seems more common when a substantial Ab-amyloid deposition is present ; this finding may explain why the postural instability and gait disorder motor phenotype is considered a risk factor for the development of PDD . In addition to cortical
abnormalities, the functional connectivity of basal ganglia  and the pedunculopontine nucleus  has been suggested to be involved in the pathophysiology of the gait disorder in PD. Another motor feature of advanced PD is the development of levodopa-induced dyskinesias (LIDs), which have been linked to presynaptic dopaminergic denervation as evaluated by [18F]FP-CIT PET , and to an immediate hypersensitivity of the pre-supplementary motor area and putamen to levodopa as assessed with functional MRI (fMRI) during a stimulus–response mapping task . Substantial pathology within the serotonergic system in PD has been demonstrated in vivo in recent years with PET and serotonergic markers such as [11C]DASB [49– 51]. Several studies have demonstrated serotonergic dysfunction that correlated with depression levels , weight changes , development of action-postural tremor  and LIDs [55, 56] in PD. Several studies in PD patients with LIDs demonstrated that striatal serotonergic terminals contribute to the development of LIDs through aberrant processing of exogenous levodopa and release of dopamine as a false neurotransmitter in the denervated striatum ; these studies also supported the development of selective serotonin receptor type 1A agonists as antidyskinetic agents in PD . [11C]DASB PET has also shown extra-striatal global brain loss of serotonergic function in transplanted PD patients with non-motor symptoms  and striatal graft-specific serotonergic hyperinnervation in those who developed graft-induced dyskinesias [58, 59]. Several neuroimaging techniques have been tested for the differential diagnosis of atypical parkinsonism (AP). [18F]FDG PET was superior to [123I]IBZM single photon emission computerized tomography (SPECT) in the differentiation of AP from Lewy body diseases (with the majority of PD) and could differentiate different AP syndromes with high specificity . FA and various diffusivity measures obtained from different structures such as the cerebellum and basal ganglia have been successfully used to discriminate PD from AP and distinguish different types of AP with high sensitivity and specificity . For example, MRI-based segmentation of long variable echotrains , supervised machine learning approach , automatic pattern recognition  and brain viscoelasticity as measured with MR-elastography  have been shown to be able to distinguish PSP from PD, although their value as clinical tools has yet to be fully assessed. The parkinsonian variant of MSA, which can be difficult to distinguish from PD, especially at early stages, can be differentiated from PD and PSP by measuring putaminal MD values . Other parameters that could help in discriminating MSA from PD are the relatively preserved volume of olfactory bulbs and tracts , and the different
contrast ratio of the LC . Altogether, these data look promising for aiding the diagnosis of AP; however, they need further validation with larger sample sizes before being integrated into novel therapeutic trials. Huntington’s disease (HD) is a progressive and fatal neurodegenerative disorder caused by an expanded trinucleotide CAG sequence in the huntingtin gene (HTT) on chromosome 4. Over the past years, MRI and PET have provided important advances in our understanding of HD . In a large HD longitudinal study, changes in several imaging measures such as whole-brain, putamen, ventricular and grey matter volume were predictive of decline in total functional capacity and tracked longitudinal change over 36 months after adjustment with respect to age and CAG length . MRI has also shown that there is an inverse correlation between volume and iron levels in the putamen, pallidus and anterior cingulate cortex, while a direct correlation is present between iron deposits and cortical volume in sensorimotor and temporo-occipital cortex ; iron deposits in the corpus callosum could be regarded as a marker of disease state, since iron accumulates only in manifest HD . Longitudinal FA change in putamen has been demonstrated to distinguish between premanifest HD and symptomatic HD after a diagnosis was established ; premanifest HD also exhibited a longitudinal reduction in functional connectivity, evaluated with BOLD signal, among an extensive prefrontal and motor network during a working memory task . Also, a spatial covariance metabolic pattern investigated with [18F]FDG has been able to evaluate disease progression . Analysis of the BOLD signal has been successfully used to reveal early functional connectivity changes in HD. Premanifest HD exhibited a decrease in the BOLD synchrony between the caudate nucleus and premotor area , and in sensorimotor and dorsal attention networks , indicating that the BOLD signal may be a useful tool for measuring early neuronal dysfunction in HD. Using structural MRI, [11C]PK11195 and [11C]raclopride PET, one study found that pathologically activated microglia in the associative striatum and other areas related to cognitive function may be a useful predictor of clinical HD onset .
Dementia Cognitive decline, particularly in the context of Alzheimer’s disease (AD), has attracted numerous recent imaging studies. Numerous imaging biomarkers have been proposed in these investigations and their clinical applications need to be validated . Alzheimer’s disease is the most common cause of dementia and is characterized by cognitive impairment, progressive neurodegeneration and
formation of Ab-amyloid containing plaques and neurofibrillary tangles (NFTs) composed of hyperphosphorylated tau . One of the most significant developments in AD research has been the development to image the amyloid plaques, whose deposition and accumulation are viewed as fundamental to the pathological process leading to AD . [11C]PiB PET has been shown to be able to image Ab-amyloid plaques in the cortex many years before the clinical onset of the disease in patients with autosomal dominant AD , and has shown higher levels of Abamyloid deposits in early onset AD compared to late onset AD . Also, Ab-amyloid deposits measured with [11C]PiB PET are predictive of cognitive decline in healthy individuals and patients with mild cognitive impairment (MCI) . Although [11C]PIB PET can be a promising imaging marker to quantify Ab-amyloid retention, its potential use in clinical practice is limited by the short halflife of C-11 (about 20 min), which requires on-site cyclotron production. This disadvantage can be overcome by the use of more recently established PET ligands such as [18F]Florbetapir, [18F]Florbetaben and [18F]Flutametamol which have a half-life of about 110 min . [18F]Florbetaben has been shown to facilitate accurate detection of prodromal AD , and [18F]Flutametamol has yielded results similar to [11C]PIB in Ab-amyloid detection . Similarly, Florbetapir could significantly improve the clinical identification of dementia in AD , and has been successfully used to detect Ab-amyloid deposits in presenilin 1vE280A mutation carriers before clinical onset . Ab-amyloid PET imaging such as with [18F]Florbetapir has been shown to be predictive of cognitive and global deterioration over a 3-year follow-up in patients with MCI and probable AD . More recently, several ligands for tau protein have been devised, such as [18F]T807 , [18F]T808  [11C]PBB3 , [18F]THK5105 ; however, their diagnostic role in neurological disorders characterized by accumulation of tau protein has not been investigated yet. Higher Ab-amyloid burden as estimated with [18F]Florbetapir correlates with decreased cerebral blood flow measured with ASL  and basal forebrain cholinergic system atrophy  in MCI and AD patients. However, Ab-amyloid deposition measured with [11C]PIB has been shown to be independent of hippocampal neurodegeneration , suggesting that degeneration of the hippocampus and Ab-amyloid deposits could be at least partially different phenomena. In patients with MCI, the combination of several biomarkers, such as Ab42 concentration in the cerebrospinal fluid, temporoparietal hypometabolism measured with FDG-PET and decreased hippocampal volume, has been shown to be linked to increased incidence of progression to dementia with increased biological severity and decreased conversion time
; cortical volumetry may also predict future decline in cognition in cognitively normal HS . Other types of degenerative dementias have been much less investigated in recent years compared to AD. Dementia with Lewy bodies (DLB) is the second most common form of neurodegenerative dementia . Patients affected by DLB exhibit marked reduction of dopaminergic activity in the basal ganglia as evaluated with different PET and SPECT tracers. Also, DLB patients show hypoperfusion of parietal and occipital regions when evaluated with SPECT techniques and structural preservation of medial temporal lobes . DLB can be differentiated from AD based on the lower density of dopamine vesicular transporters as estimated with [18F]AV133  and relatively preserved temporal lobe perfusion as measured with ASL . Frontotemporal lobar degeneration (FTLD) is a heterogeneous group of neurodegenerative conditions that share clinical features and pathologic and genetic etiologies . Using a ROI-based classification which combed information from FDG-PET and MRI information, an accuracy of 94 % for the differentiation of AD and FTLD patients was obtained . Also, [18F]Florbetaben has been shown to distinguish patients with FTLD from AD . Other imaging modalities, such as fMRI, have been used as a tool to investigate preclinical alterations in dementia. Several outcome measures have been used, like changes in hippocampal activity during memory-related tasks and resting-state fMRI, which have been reviewed elsewhere [106, 107]. In particular, paradoxically increased hippocampal activity may be an early indicator of AD-related neurodegeneration in a distributed network . From the present data, it seems that imaging techniques, and in particular amyloid imaging, has greatly contributed to the characterization of AD, while the significance of recent imaging literature on other form of dementia seems limited. fMRI, despite technical advancements and interesting pathophysiological investigation, still has a less established clinical role and needs validation in future studies.
Epilepsy With 65 million people affected worldwide, epilepsy is a very common and chronic neurological disease . Recent use of neuroimaging in epilepsy has indicated that seizure activity propagates along specific anatomical pathways that characterize the underlying epilepsy syndrome . For instance, temporal lobe epilepsy (TLE) appears to chronically alter the activity of several brainwide neural networks involved in the control of higher
order brain functions and not traditionally linked to epilepsy, like the default mode network (DMN), attention networks, executive control network and reward network when investigated with resting-state fMRI . Connectivity alterations within these networks have been suggested to be responsible for the cognitive and psychiatric symptoms of TLE like depression . In particular, auditory memory deficits have been linked to diminished 5-HT1A receptor binding measured with [18F]FCWAY , while depression was associated with decreased serotonin transporter activity in the insular cortex evaluated with [11C]DASB . Neuroimaging techniques have also proven to be useful in the setting of surgical evaluation of TLE. In this context, the combined use of electrical source imaging, perfusion evaluated with ASL and [18F]FDG PET may play an increasingly important role in the non-invasive evaluation of patients with refractory focal epilepsy . In particular, patients free of seizures after surgery had significantly different mesial temporal asymmetry for 5-HT1A receptor densities as evaluated with [18F]FCWAY and glucose metabolism measured with [18F]FDG PET . [11C]Flumazenil PET provides evidence for a more restricted region of abnormality in patients with drug-resistant temporal lobe epilepsy and thus give localizing information which is complimentary to [18F]FDG PET . Overall, it seems that surgical approaches to intractable epilepsy could benefit from the use of PET, although more studies in conjunction with electroencephalographic mapping of epileptogenic areas are needed. Recent studies have also shed light on the genetic and malformative causes of epilepsy. In patients with tuberous sclerosis complex and intractable epilepsy, the more hotspots of a-[11C]-methyl-L-tryptophan ([11C]AMT), which is used for the measurement of serotonin synthesis, correlated with longer duration of seizure intractability ; in another study, higher binding of [11C]AMT correlated negatively with FA values . PET using [11C]PK11195 was able to detect neuroinflammation associated with subtle focal cortical dysplasia in one study . Cortical dysplasia also displays hyperperfusion, which can be related to increased microvessel density and can be monitored with ASL .
Traumatic brain injury The incidence of traumatic brain injury (TBI) is increasing worldwide, and consequences of severe TBI such as permanent disability can be detrimental, especially in children . Ab-amyloid depositions are very often found in post mortem examination of subjects who had severe TBI; although Ab-amyloid deposits are gradually cleared during
the days and months following acute TBI, autopsy studies indicate that Ab plaques (often of the AD-associated form, Ab 42) are present in up to 30 % of patients with a history of TBI, regardless of age . These findings are in line with epidemiological data suggesting that TBI results in a substantial increase in the risk for developing AD later in life . PET with [11C]PiB following acute TBI is in agreement with the distribution of Ab-amyloid deposition in the postmortem tissue of patients with TBI . However, the relationship between acute traumatic brain injury (TBI) and Ab-amyloid deposition is still incompletely understood. A subcortical tauopathy with a distribution that mimics that of PSP, with the addition of hippocampal involvement, was observed in a case of chronic traumatic encephalopathy; also, focal Ab-amyloid aggregation after a single, severe TBI has been found . Moreover, one study found that not all patients who had undergone TBI showed [11C]PiB binding; in patients, in whom [11C]PiB binding was present, binding was not correlated with chronic neuropsychological impairment, severity of injury, computed tomography findings, elapsed time from the injury, and cognitive performance . Despite the importance of defining the relationship between TBI and Ab-amyloid depositions, the clinical application of Ab-amyloid imaging needs further clarification. Important information about the role of the inflammatory response after TBI came from a study which showed an increase in binding of [11C]PK11195 months or years after TBI, suggesting that the traumatic event triggers a chronic inflammatory response, associated with a greater cognitive deficit . Several recent studies used different MRI techniques in the investigation of the underlying pathophysiology of TBI. Analysis of resting-state fMRI demonstrated alterations of multiple brain networks in patients with mild TBI in the acute stage, suggesting that fMRI could be used as a potential biomarker for improved detection of TBI in the acute setting [128, 129]. Connectivity alterations of neural structures included in the DMN has been also reported in repetitive sub-concussive events in athletes  and in animal models of TBI . FA has received attention in the clinical context of TBI. Patients with mild TBI showed evidence of increased FA in the bilateral superior frontal cortices during the semiacute phase . One metaanalysis study suggested that acute mild TBI is associated with elevated FA values and that complaints due to chronic mild TBI are correlated with depressed anisotropy . According to other data, FA was predictive of functional recovery after TBI . However, the clinical usefulness of FA in TBI has been questioned by a large study in patients with mild TBI , which showed that acute mild TBI was not associated with DTI abnormalities. Entropy measurement, which is more sensitive to axonal
density and orientation, could be more effective in distinguishing axonal remodeling after injury when compared with FA . Several studies have investigated the DMN in patients with TBI. Patients who have undergone TBI showed an increase in functional connectivity in the DMN, with higher DMN functional connectivity being associated to lesser cognitive impairment . A lower DMN connectivity was found in patients who showed more diffuse axonal injury within the corpus callosum measured with DTI . TBI patients with impaired cognitive control showed reduced functional connectivity on fMRI between DMN and right anterior insula, an area critically involved in the salience network, during a response inhibition and switching task .
Multiple sclerosis Multiple sclerosis (MS) is a chronic, inflammatory demyelinating disease, often resulting in considerable disability among affected patients. Imaging research in MS has recently evolved to address the issue of visualizing neuroinflammation and changes in myelin content in vivo. [11C]PK11195 PET has showed increased microglial activation in the cortical grey matter (GM) of relapsingremitting (RR) and secondary progressive (SP) MS patients, which correlated with levels of neurological disability . Patients with clinically isolated syndrome showed a global increase of [11C]PK11195 binding in normal-appearing white matter (NAWM), and the binding was higher in subjects who developed lesions that were disseminated in space or MS at 2 years follow-up . [11C]PK11195 PET has been found to be increased in chronic hypointense T1-weighted MRI lesions (black holes) and this has been associated with worse prognosis at 2 years of follow-up in progressive MS . [11C]PK11195 PET was also able to show decreases in microglial activation after 1 year of treatment with glatiramer acetate in RRMS patients . These findings suggest that [11C]PK11195 PET is able to detect diffuse widespread inflammation and chronic active plaques, which are two characteristics associated with progressive disease, and may predict disability progression. These promising studies could help in the future towards better management of MS patients, through monitoring disease progression and testing of anti-inflammatory drugs. Second generation TSPO radioligands such as 11 [ C]DAA1106, [18F]FEDAA1106, [11C]PBR28 and [18F]PBR111 have recently been developed to address the limitations associated with [11C]PK11195 such as low signal to noise ratio [142–144]. Increased [18F]PBR111 binding has been found in MS lesions and in perilesional
white matter (WM) volumes of MS patients compared to healthy controls . Within-subject analysis showed higher [18F]PBR111 binding in WM lesions and perilesional regions compared to the normal WM, which was associated with worse disability scores . These data suggest that TSPO PET with second generation radioligands can be used to assess in vivo inflammatory response in MS and provides further evidence supporting the role of microglial activation in disease progression. PET imaging with the development of new radiolabelled ligands may also allow further applications in MS . For example, PET could be used for the quantitative assessment of myelination, using a family of Congo red derivatives. A recent in vivo PET study has assessed [11C]PiB as a suitable biomarker for imaging myelin in normal nonhuman primates and in two patients with RRMS . [11C]PiB has shown high affinity for CNS myelin in the normal nonhuman primates. In RRMS patients, higher [11C]PiB binding was detected in the normal appearing WM compared to GM structures, and showed reduced uptake in WM lesions compared to normal appearing WM. Interestingly, [11C]PiB uptake in gadolinium-enhancing lesions tends to be higher than in non-enhancing lesions, suggesting a different magnitude of myelin loss between chronically demyelinated lesions and more recent plaques that do not achieve complete demyelination . Imaging remyelination would be very important in MS to monitor the disease evolution and to assess putative remyelinating strategies. MRI provides valuable support in clinical management of MS, but an increment in lesion load on conventional MRI is not necessarily associated with clinical manifestations; thus, nonconventional MRI techniques as biomarkers of MS are increasingly been considered . Currently, new MRI techniques are mostly aimed to increasing the specificity for the type of underlying pathology, to developing improved predictive markers for disease activity and disability, and to studying grey matter pathology . Recently, quantitative MRI techniques have been developed with the prospect of direct assessment of myelination status. Multi-component driven equilibrium single pulse observation of T1 and T2 (mcDESPOT) allows quantitative estimation of the myelin water fraction (MWF) in scan times of less than 15 min [149–151]. In primary progressive (PP) MS, RRMS and SPMS lower MWF values were associated with higher disability measures, including cognitive assessments and sensory scores . The morphological correlates of disability in MS have been addressed with several MRI applications. Disability shows correlations with brain T2 lesion load, but correlates less consistently with grey matter atrophy . Disability correlated with lesion load quantified with gradient echo plural contrast imaging in one study .
DTI is an effective means of quantifying parameters of demyelination and axonal loss, and its application in MS has yielded noteworthy results in recent years . MS patients have significantly lower FA and higher MD values in their NAWM and cerebral cortex; these changes are more marked in SPMS than in RRMS patients . DTI indices in the optic radiations have also been suggested to correlate with disability . By contrast, DTI measures obtained by areas around lesions and NAWM have shown no correlation with clinical status . The proton magnetic resonance spectroscopy imaging (1H-MRSI) allows the evaluation of different cerebral metabolites in vivo . A decreased ratio between Nacetylaspartate (NAA) and creatine, suggestive of axonal damage, has been reported in patients with radiologically isolated syndrome . Choline, a marker of membrane phospholipids synthesis and degradation, correlates with phosholipid release during active demyelination and can as well be quantified with 1H-MRSI [159, 160]. Myo-inositol (mI), synthesized in glial cells and associated with gliosis , can be also investigated with 1H-MRSI. The mI/ NAA ratio in NAWM has been shown to predict brainvolume loss and clinical disability in patients with various subtypes of MS . Other interesting molecules investigated by 1H-MRSI in the MS setting are glutathione, which is useful for the evaluation of oxidative stress and which has been found to be reduced in patients with SPMS ; lipids, used as a putative index of remyelination, have been studied in NAWM of patients with PPMS ; and macromolecules, which may represent markers of myelin fragments . Overall, although the use of 1HMRSI is not common because of technical demands , the ability to study many different molecules in vivo makes it a very promising tool to investigate the biochemical alterations linked to demyelination.
Conclusions Over the recent years, a significant number of novel or established MRI and PET techniques have been applied to common neurological disorders. These techniques have been extensively used to study the pathophysiology of complex mechanisms and to aid in the diagnosis and management of patients. Some of these techniques, such as novel PET tracers in AD, have gained an established clinical role. The recent literature also discloses very promising role for imaging techniques in the differential diagnosis of different parkinsonian syndromes and in the investigation of the mechanisms underlying MS. Other imaging modalities, such as resting-state fMRI, have a less established clinical role and their contribution to the
management of patients needs to be systematically evaluated. Acknowledgments There is no funding related to this article. L.R. has been supported by the Edmond J. and Lily Safra Foundation. F.N. has been supported by the Parkinson’s UK. M.P. research has been supported by the Edmond J. Safra Foundation, Michael J. Fox Foundation, Parkinson’s UK, Imanova Ltd, and the National Institute for Health Research Biomedical Research Centre. Conflicts of interest of interest.
The authors declare that they have no conflict
References 1. Politis M, Piccini P (2012) Positron emission tomography imaging in neurological disorders. J Neurol 259:1769–1780. doi:10.1007/s00415-012-6428-3 2. Loane C, Politis M (2011) Positron emission tomography neuroimaging in Parkinson’s disease. Am J Transl Res 3:323–341 3. Politis M (2014) Neuroimaging in Parkinson disease: from research setting to clinical practice. Nat Rev Neurol. doi:10.1038/ nrneurol.2014.205 4. Niccolini F, Su P, Politis M (2014) Dopamine receptor mapping with PET imaging in Parkinson’s disease. J Neurol. doi:10.1007/ s00415-014-7302-2 5. Hsiao I-T, Weng Y-H, Hsieh C-J et al (2014) Correlation of Parkinson disease severity and 18F-DTBZ positron emission tomography. JAMA Neurol 71:758–766. doi:10.1001/jama neurol.2014.290 6. Lin S-C, Lin K-J, Hsiao I-T et al (2014) In vivo detection of monoaminergic degeneration in early Parkinson disease by (18)F-9-fluoropropyl-(?)-dihydrotetrabenzazine PET. J Nucl Med 55:73–79. doi:10.2967/jnumed.113.121897 7. Ogisu K, Kudo K, Sasaki M et al (2013) 3D neuromelaninsensitive magnetic resonance imaging with semi-automated volume measurement of the substantia nigra pars compacta for diagnosis of Parkinson’s disease. Neuroradiology 55:719–724. doi:10.1007/s00234-013-1171-8 8. Ohtsuka C, Sasaki M, Konno K et al (2014) Differentiation of early-stage parkinsonisms using neuromelanin-sensitive magnetic resonance imaging. Parkinsonism Relat Disord 20:755–760. doi:10.1016/j.parkreldis.2014.04.005 9. Bunzeck N, Singh-Curry V, Eckart C et al (2013) Motor phenotype and magnetic resonance measures of basal ganglia iron levels in Parkinson’s disease. Parkinsonism Relat Disord 19:1136–1142. doi:10.1016/j.parkreldis.2013.08.011 10. Ulla M, Bonny JM, Ouchchane L et al (2013) Is R2* a new MRI biomarker for the progression of Parkinson’s disease? A longitudinal follow-up. PLoS One 8:e57904. doi:10.1371/journal. pone.0057904 11. Teune LK, Renken RJ, de Jong BM et al (2014) Parkinson’s disease-related perfusion and glucose metabolic brain patterns identified with PCASL-MRI and FDG-PET imaging. Neuroimage Clin 5:240–244. doi:10.1016/j.nicl.2014.06.007 12. Scherfler C, Esterhammer R, Nocker M et al (2013) Correlation of dopaminergic terminal dysfunction and microstructural abnormalities of the basal ganglia and the olfactory tract in Parkinson’s disease. Brain 136:3028–3037. doi:10.1093/brain/ awt234 13. Lenfeldt N, Hansson W, Larsson A et al (2013) Diffusion tensor imaging and correlations to Parkinson rating scales. J Neurol 260:2823–2830. doi:10.1007/s00415-013-7080-2
J Neurol 14. Ziegler E, Rouillard M, Andre´ E et al (2014) Mapping track density changes in nigrostriatal and extranigral pathways in Parkinson’s disease. Neuroimage 99:498–508. doi:10.1016/j. neuroimage.2014.06.033 15. Gaenslen A, Unmuth B, Godau J et al (2008) The specificity and sensitivity of transcranial ultrasound in the differential diagnosis of Parkinson’s disease: a prospective blinded study. Lancet Neurol 7:417–424. doi:10.1016/S1474-4422(08)70067-X 16. Berg D, Godau J, Walter U (2008) Transcranial sonography in movement disorders. Lancet Neurol 7:1044–1055. doi:10.1016/ S1474-4422(08)70239-4 17. Liu P, Li X, Li F-F et al (2014) The predictive value of transcranial sonography in clinically diagnosed patients with early stage Parkinson’s disease: comparison with DAT PET scans. Neurosci Lett 582:99–103. doi:10.1016/j.neulet.2014.08.053 18. Bouwmans AEP, Vlaar AMM, Mess WH et al (2013) Specificity and sensitivity of transcranial sonography of the substantia nigra in the diagnosis of Parkinson’s disease: prospective cohort study in 196 patients. BMJ Open. doi:10.1136/bmjopen-2013-002613 19. Sanzaro E, Iemolo F, Duro G, Malferrari G (2014) A new assessment tool for Parkinson disease: the nigral lesion load obtained by transcranial sonography. J Ultrasound Med 33:1635–1640. doi:10.7863/ultra.33.9.1635 20. Winter Y, von Campenhausen S, Arend M et al (2011) Healthrelated quality of life and its determinants in Parkinson’s disease: results of an Italian cohort study. Parkinsonism Relat Disord 17:265–269. doi:10.1016/j.parkreldis.2011.01.003 21. Petrou M, Bohnen NI, Mu¨ller MLTM et al (2012) Ab-amyloid deposition in patients with Parkinson disease at risk for development of dementia. Neurology 79:1161–1167. doi:10.1212/ WNL.0b013e3182698d4a 22. Gomperts SN, Locascio JJ, Rentz D et al (2013) Amyloid is linked to cognitive decline in patients with Parkinson disease without dementia. Neurology 80:85–91. doi:10.1212/WNL. 0b013e31827b1a07 23. Niethammer M, Tang CC, Ma Y et al (2013) Parkinson’s disease cognitive network correlates with caudate dopamine. Neuroimage 78:204–209. doi:10.1016/j.neuroimage.2013.03.070 24. Politis M, Su P, Piccini P (2012) Imaging of microglia in patients with neurodegenerative disorders. Front Pharmacol 3:96. doi:10.3389/fphar.2012.00096 25. Ratchford JN, Endres CJ, Hammoud DA et al (2012) Decreased microglial activation in MS patients treated with glatiramer acetate. J Neurol 259:1199–1205. doi:10.1007/s00415-011-6337-x 26. Rissanen E, Tuisku J, Rokka J et al (2014) In vivo detection of diffuse inflammation in secondary progressive multiple sclerosis using PET imaging and the radioligand 11C-PK11195. J Nucl Med 55:939–944. doi:10.2967/jnumed.113.131698 27. Edison P, Ahmed I, Fan Z et al (2013) Microglia, amyloid, and glucose metabolism in Parkinson’s disease with and without dementia. Neuropsychopharmacology 38:938–949. doi:10.1038/ npp.2012.255 28. Fan Z, Aman Y, Ahmed I et al (2014) Influence of microglial activation on neuronal function in Alzheimer’s and Parkinson’s disease dementia. Alzheimers Dement. doi:10.1016/j.jalz.2014.06.016 29. Hong JY, Oh JS, Lee I et al (2014) Presynaptic dopamine depletion predicts levodopa-induced dyskinesia in de novo Parkinson disease. Neurology 82:1597–1604. doi:10.1212/ WNL.0000000000000385 30. Pagonabarraga J, Corcuera-Solano I, Vives-Gilabert Y et al (2013) Pattern of regional cortical thinning associated with cognitive deterioration in Parkinson’s disease. PLoS One 8:e54980. doi:10.1371/journal.pone.0054980 31. Mak E, Su L, Williams GB, O’Brien JT (2014) Neuroimaging characteristics of dementia with Lewy bodies. Alzheimers Res Ther 6:18. doi:10.1186/alzrt248
32. Zarei M, Ibarretxe-Bilbao N, Compta Y et al (2013) Cortical thinning is associated with disease stages and dementia in Parkinson’s disease. J Neurol Neurosurg Psychiatr 84:875–881. doi:10.1136/jnnp-2012-304126 33. Pereira JB, Svenningsson P, Weintraub D et al (2014) Initial cognitive decline is associated with cortical thinning in early Parkinson disease. Neurology 82:2017–2025. doi:10.1212/ WNL.0000000000000483 34. Morales DA, Vives-Gilabert Y, Go´mez-Anso´n B et al (2013) Predicting dementia development in Parkinson’s disease using Bayesian network classifiers. Psychiatry Res 213:92–98. doi:10. 1016/j.pscychresns.2012.06.001 35. Hanganu A, Bedetti C, Degroot C et al (2014) Mild cognitive impairment is linked with faster rate of cortical thinning in patients with Parkinson’s disease longitudinally. Brain 137:1120–1129. doi:10.1093/brain/awu036 36. Kandiah N, Zainal NH, Narasimhalu K et al (2014) Hippocampal volume and white matter disease in the prediction of dementia in Parkinson’s disease. Parkinsonism Relat Disord. doi:10.1016/j.parkreldis.2014.08.024 37. Deng B, Zhang Y, Wang L et al (2013) Diffusion tensor imaging reveals white matter changes associated with cognitive status in patients with Parkinson’s disease. Am J Alzheimers Dis Other Demen 28:154–164. doi:10.1177/1533317512470207 38. Agosta F, Canu E, Stefanova E et al (2014) Mild cognitive impairment in Parkinson’s disease is associated with a distributed pattern of brain white matter damage. Hum Brain Mapp 35:1921–1929. doi:10.1002/hbm.22302 39. O’Sullivan SS, Wu K, Politis M et al (2011) Cue-induced striatal dopamine release in Parkinson’s disease-associated impulsivecompulsive behaviours. Brain 134:969–978. doi:10.1093/brain/ awr003 40. Politis M, Loane C, Wu K et al (2013) Neural response to visual sexual cues in dopamine treatment-linked hypersexuality in Parkinson’s disease. Brain 136:400–411. doi:10.1093/brain/ aws326 41. Bohnen NI, Frey KA, Studenski S et al (2013) Gait speed in Parkinson disease correlates with cholinergic degeneration. Neurology 81:1611–1616. doi:10.1212/WNL.0b013e3182a9f558 42. Bohnen NI, Jahn K (2013) Imaging: what can it tell us about parkinsonian gait? Mov Disord 28:1492–1500. doi:10.1002/ mds.25534 43. Bohnen NI, Frey KA, Studenski S et al (2014) Extra-nigral pathological conditions are common in Parkinson’s disease with freezing of gait: an in vivo positron emission tomography study. Mov Disord 29:1118–1124. doi:10.1002/mds.25929 44. Mu¨ller MLTM, Frey KA, Petrou M et al (2013) b-Amyloid and postural instability and gait difficulty in Parkinson’s disease at risk for dementia. Mov Disord 28:296–301. doi:10.1002/mds. 25213 45. Shine JM, Matar E, Ward PB et al (2013) Freezing of gait in Parkinson’s disease is associated with functional decoupling between the cognitive control network and the basal ganglia. Brain 136:3671–3681. doi:10.1093/brain/awt272 46. Fling BW, Cohen RG, Mancini M et al (2013) Asymmetric pedunculopontine network connectivity in parkinsonian patients with freezing of gait. Brain 136:2405–2418. doi:10.1093/brain/ awt172 47. Hong JY, Yun HJ, Sunwoo MK et al (2014) Cognitive and cortical thinning patterns of subjective cognitive decline in patients with and without Parkinson’s disease. Parkinsonism Relat Disord 20:999–1003. doi:10.1016/j.parkreldis.2014.06.011 48. Herz DM, Haagensen BN, Christensen MS et al (2014) The acute brain response to levodopa heralds dyskinesias in Parkinson disease. Ann Neurol 75:829–836. doi:10.1002/ana. 24138
J Neurol 49. Politis M, Niccolini F (2014) Serotonin in Parkinson’s disease. Behav Brain Res. doi:10.1016/j.bbr.2014.07.037 50. Politis M, Loane C (2011) Serotonergic dysfunction in Parkinson’s disease and its relevance to disability. Sci World J 11:1726–1734. doi:10.1100/2011/172893 51. Politis M, Wu K, Loane C et al (2010) Staging of serotonergic dysfunction in Parkinson’s disease: an in vivo 11C-DASB PET study. Neurobiol Dis 40:216–221. doi:10.1016/j.nbd.2010.05. 028 52. Politis M, Wu K, Loane C et al (2010) Depressive symptoms in PD correlate with higher 5-HTT binding in raphe and limbic structures. Neurology 75:1920–1927. doi:10.1212/WNL. 0b013e3181feb2ab 53. Politis M, Loane C, Wu K et al (2011) Serotonergic mediated body mass index changes in Parkinson’s disease. Neurobiol Dis 43:609–615. doi:10.1016/j.nbd.2011.05.009 54. Loane C, Wu K, Bain P et al (2013) Serotonergic loss in motor circuitries correlates with severity of action-postural tremor in PD. Neurology 80:1850–1855 55. Politis M, Wu K, Loane C et al (2014) Serotonergic mechanisms responsible for levodopa-induced dyskinesias in Parkinson’s disease patients. J Clin Invest 124:1340–1349. doi:10.1172/ JCI71640 56. Niccolini F, Loane C, Politis M (2014) Dyskinesias in Parkinson’s disease: views from positron emission tomography studies. Eur J Neurol 21(694–699):e39–e43. doi:10.1111/ene.12362 57. Politis M, Wu K, Loane C et al (2012) Serotonin neuron loss and nonmotor symptoms continue in Parkinson’s patients treated with dopamine grafts. Sci Transl Med 4:128ra41. doi:10.1126/ scitranslmed.3003391 58. Politis M, Wu K, Loane C et al (2010) Serotonergic neurons mediate dyskinesia side effects in Parkinson’s patients with neural transplants. Sci Transl Med 2:38ra46. doi:10.1126/sci translmed.3000976 59. Politis M, Oertel WH, Wu K et al (2011) Graft-induced dyskinesias in Parkinson’s disease: high striatal serotonin/dopamine transporter ratio. Mov Disord 26:1997–2003. doi:10.1002/mds.23743 60. Hellwig S, Amtage F, Kreft A et al (2012) [18F]FDG-PET is superior to [123I]IBZM-SPECT for the differential diagnosis of parkinsonism. Neurology 79:1314–1322. doi:10.1212/WNL. 0b013e31826c1b0a 61. Prodoehl J, Li H, Planetta PJ et al (2013) Diffusion tensor imaging of Parkinson’s disease, atypical parkinsonism, and essential tremor. Mov Disord 28:1816–1822. doi:10.1002/mds. 25491 62. Hara K, Watanabe H, Ito M et al (2014) Potential of a new MRI for visualizing cerebellar involvement in progressive supranuclear palsy. Parkinsonism Relat Disord 20:157–161. doi:10. 1016/j.parkreldis.2013.10.007 63. Salvatore C, Cerasa A, Castiglioni I et al (2014) Machine learning on brain MRI data for differential diagnosis of Parkinson’s disease and progressive supranuclear palsy. J Neurosci Methods 222:230–237. doi:10.1016/j.jneumeth.2013.11. 016 64. Cherubini A, Morelli M, Nistico´ R et al (2014) Magnetic resonance support vector machine discriminates between Parkinson disease and progressive supranuclear palsy. Mov Disord 29:266–269. doi:10.1002/mds.25737 65. Lipp A, Trbojevic R, Paul F et al (2013) Cerebral magnetic resonance elastography in supranuclear palsy and idiopathic Parkinson’s disease. Neuroimage Clin 3:381–387. doi:10.1016/j. nicl.2013.09.006 66. Baudrexel S, Seifried C, Penndorf B et al (2014) The value of putaminal diffusion imaging versus 18-fluorodeoxyglucose positron emission tomography for the differential diagnosis of
the Parkinson variant of multiple system atrophy. Mov Disord 29:380–387. doi:10.1002/mds.25749 Chen S, Tan H, Wu Z et al (2014) Imaging of olfactory bulb and gray matter volumes in brain areas associated with olfactory function in patients with Parkinson’s disease and multiple system atrophy. Eur J Radiol 83:564–570. doi:10.1016/j.ejrad.2013. 11.024 Matsuura K, Maeda M, Yata K et al (2013) Neuromelanin magnetic resonance imaging in Parkinson’s disease and multiple system atrophy. Eur Neurol 70:70–77. doi:10.1159/000350291 Niccolini F, Politis M (2014) Neuroimaging in Huntington’s disease. World J Radiol 6:301–312. doi:10.4329/wjr.v6.i6.301 Tabrizi SJ, Scahill RI, Owen G et al (2013) Predictors of phenotypic progression and disease onset in premanifest and earlystage Huntington’s disease in the TRACK-HD study: analysis of 36-month observational data. Lancet Neurol 12:637–649. doi:10.1016/S1474-4422(13)70088-7 Sa´nchez-Castan˜eda C, Squitieri F, Di Paola M et al (2014) The role of iron in gray matter degeneration in Huntington’s disease: a magnetic resonance imaging study. Hum Brain Mapp. doi:10. 1002/hbm.22612 Di Paola M, Phillips OR, Sanchez-Castaneda C et al (2014) MRI measures of corpus callosum iron and myelin in early Huntington’s disease. Hum Brain Mapp 35:3143–3151. doi:10.1002/ hbm.22391 Domı´nguez DJF, Egan GF, Gray MA et al (2013) Multi-modal neuroimaging in premanifest and early Huntington’s disease: 18 month longitudinal data from the IMAGE-HD study. PLoS One 8:e74131. doi:10.1371/journal.pone.0074131 Georgiou-Karistianis N, Poudel GR, Domı´nguez DJF et al (2013) Functional and connectivity changes during working memory in Huntington’s disease: 18 month longitudinal data from the IMAGE-HD study. Brain Cogn 83:80–91. doi:10.1016/ j.bandc.2013.07.004 Tang CC, Feigin A, Ma Y et al (2013) Metabolic network as a progression biomarker of premanifest Huntington’s disease. J Clin Invest 123:4076–4088. doi:10.1172/JCI69411 Unschuld PG, Joel SE, Liu X et al (2012) Impaired corticostriatal functional connectivity in prodromal Huntington’s disease. Neurosci Lett 514:204–209. doi:10.1016/j.neulet.2012.02. 095 Poudel GR, Egan GF, Churchyard A et al (2014) Abnormal synchrony of resting state networks in premanifest and symptomatic Huntington disease: the IMAGE-HD study. J Psychiatry Neurosci 39:87–96 Politis M, Pavese N, Tai YF et al (2011) Microglial activation in regions related to cognitive function predicts disease onset in Huntington’s disease: a multimodal imaging study. Hum Brain Mapp 32:258–270 McConathy J, Sheline YI (2014) Imaging biomarkers associated with cognitive decline: a review. Biol Psychiatry. doi:10.1016/j. biopsych.2014.08.024 Hardy J (2006) Alzheimer’s disease: the amyloid cascade hypothesis: an update and reappraisal. J Alzheimers Dis 9:151–153 Adlard PA, Tran BA, Finkelstein DI et al (2014) A review of bamyloid neuroimaging in Alzheimer’s disease. Front Neurosci 8:327. doi:10.3389/fnins.2014.00327 Benzinger TLS, Blazey T, Jack CR et al (2013) Regional variability of imaging biomarkers in autosomal dominant Alzheimer’s disease. Proc Natl Acad Sci USA 110:E4502–E4509. doi:10.1073/pnas.1317918110 Cho H, Seo SW, Kim J-H et al (2013) Amyloid deposition in early onset versus late onset Alzheimer’s disease. J Alzheimers Dis 35:813–821. doi:10.3233/JAD-121927 Lim YY, Maruff P, Pietrzak RH et al (2014) Effect of amyloid on memory and non-memory decline from preclinical to clinical
Alzheimer’s disease. Brain 137:221–231. doi:10.1093/brain/ awt286 Kung HF, Choi SR, Qu W et al (2010) 18F stilbenes and styrylpyridines for PET imaging of A beta plaques in Alzheimer’s disease: a miniperspective. J Med Chem 53:933–941. doi:10.1021/jm901039z Ong KT, Villemagne VL, Bahar-Fuchs A et al (2014) Ab imaging with 18F-florbetaben in prodromal Alzheimer’s disease: a prospective outcome study. J Neurol Neurosurg Psychiatr. doi:10.1136/jnnp-2014-308094 Hatashita S, Yamasaki H, Suzuki Y et al (2014) [18F]Flutemetamol amyloid-beta PET imaging compared with [11C]PIB across the spectrum of Alzheimer’s disease. Eur J Nucl Med Mol Imaging 41:290–300. doi:10.1007/s00259-0132564-y Beach TG, Schneider JA, Sue LI et al (2014) Theoretical impact of Florbetapir (18F) amyloid imaging on diagnosis of Alzheimer dementia and detection of preclinical cortical amyloid. J Neuropathol Exp Neurol 73:948–953. doi:10.1097/NEN. 0000000000000114 Fleisher AS, Chen K, Quiroz YT et al (2012) Florbetapir PET analysis of amyloid-b deposition in the presenilin 1 E280A autosomal dominant Alzheimer’s disease kindred: a cross-sectional study. Lancet Neurol 11:1057–1065. doi:10.1016/S14744422(12)70227-2 Doraiswamy PM, Sperling RA, Johnson K et al (2014) Florbetapir F 18 amyloid PET and 36-month cognitive decline:a prospective multicenter study. Mol Psychiatry 19:1044–1051. doi:10.1038/mp.2014.9 Xia C-F, Arteaga J, Chen G et al (2013) [(18)F]T807, a novel tau positron emission tomography imaging agent for Alzheimer’s disease. Alzheimers Dement 9:666–676. doi:10.1016/j. jalz.2012.11.008 Chien DT, Szardenings AK, Bahri S et al (2014) Early clinical PET imaging results with the novel PHF-tau radioligand [F18]T808. J Alzheimers Dis 38:171–184. doi:10.3233/JAD-130098 Maruyama M, Shimada H, Suhara T et al (2013) Imaging of tau pathology in a tauopathy mouse model and in Alzheimer patients compared to normal controls. Neuron 79:1094–1108. doi:10.1016/j.neuron.2013.07.037 Okamura N, Furumoto S, Fodero-Tavoletti MT et al (2014) Non-invasive assessment of Alzheimer’s disease neurofibrillary pathology using 18F-THK5105 PET. Brain 137:1762–1771. doi:10.1093/brain/awu064 Mattsson N, Tosun D, Insel PS et al (2014) Association of brain amyloid-b with cerebral perfusion and structure in Alzheimer’s disease and mild cognitive impairment. Brain 137:1550–1561. doi:10.1093/brain/awu043 Teipel S, Heinsen H, Amaro E et al (2014) Cholinergic basal forebrain atrophy predicts amyloid burden in Alzheimer’s disease. Neurobiol Aging 35:482–491. doi:10.1016/j.neurobiola ging.2013.09.029 Jack CR, Wiste HJ, Knopman DS et al (2014) Rates of bamyloid accumulation are independent of hippocampal neurodegeneration. Neurology 82:1605–1612. doi:10.1212/WNL. 0000000000000386 Prestia A, Caroli A, van der Flier WM et al (2013) Prediction of dementia in MCI patients based on core diagnostic markers for Alzheimer disease. Neurology 80:1048–1056. doi:10.1212/ WNL.0b013e3182872830 Toledo JB, Weiner MW, Wolk DA et al (2014) Neuronal injury biomarkers and prognosis in ADNI subjects with normal cognition. Acta Neuropathol Commun 2:26. doi:10.1186/20515960-2-26 McKeith IG (2006) Consensus guidelines for the clinical and pathologic diagnosis of dementia with Lewy bodies (DLB):
report of the Consortium on DLB International Workshop. J Alzheimers Dis 9:417–423 Siderowf A, Pontecorvo MJ, Shill HA et al (2014) PET imaging of amyloid with Florbetapir F 18 and PET imaging of dopamine degeneration with 18F-AV-133 (florbenazine) in patients with Alzheimer’s disease and Lewy body disorders. BMC Neurol 14:79. doi:10.1186/1471-2377-14-79 Binnewijzend MAA, Kuijer JPA, van der Flier WM et al (2014) Distinct perfusion patterns in Alzheimer’s disease, frontotemporal dementia and dementia with Lewy bodies. Eur Radiol 24:2326–2333. doi:10.1007/s00330-014-3172-3 Josephs KA (2008) Frontotemporal dementia and related disorders: deciphering the enigma. Ann Neurol 64:4–14. doi:10. 1002/ana.21426 Dukart J, Mueller K, Horstmann A et al (2011) Combined evaluation of FDG-PET and MRI improves detection and differentiation of dementia. PLoS One 6:e18111. doi:10.1371/ journal.pone.0018111 Villemagne VL, Ong K, Mulligan RS et al (2011) Amyloid imaging with (18)F-florbetaben in Alzheimer disease and other dementias. J Nucl Med 52:1210–1217. doi:10.2967/jnumed.111. 089730 Chhatwal JP, Sperling RA (2012) Functional MRI of mnemonic networks across the spectrum of normal aging, mild cognitive impairment, and Alzheimer’s disease. J Alzheimers Dis 31(Suppl 3):S155–S167. doi:10.3233/JAD-2012-120730 Sheline YI, Raichle ME (2013) Resting state functional connectivity in preclinical Alzheimer’s disease. Biol Psychiatry 74:340–347. doi:10.1016/j.biopsych.2012.11.028 Putcha D, Brickhouse M, O’Keefe K et al (2011) Hippocampal hyperactivation associated with cortical thinning in Alzheimer’s disease signature regions in non-demented elderly adults. J Neurosci 31:17680–17688. doi:10.1523/JNEUROSCI.474011.2011 Moshe´ SL, Perucca E, Ryvlin P, Tomson T (2014) Epilepsy: new advances. Lancet. doi:10.1016/S0140-6736(14)60456-6 Abela E, Rummel C, Hauf M et al (2014) Neuroimaging of epilepsy: lesions, networks, oscillations. Clin Neuroradiol 24:5–15. doi:10.1007/s00062-014-0284-8 Cataldi M, Avoli M, de Villers-Sidani E (2013) Resting state networks in temporal lobe epilepsy. Epilepsia 54:2048–2059. doi:10.1111/epi.12400 Chen S, Wu X, Lui S et al (2012) Resting-state fMRI study of treatment-naı¨ve temporal lobe epilepsy patients with depressive symptoms. Neuroimage 60:299–304. doi:10.1016/j.neuroimage. 2011.11.092 Theodore WH, Wiggs EA, Martinez AR et al (2012) Serotonin 1A receptors, depression, and memory in temporal lobe epilepsy. Epilepsia 53:129–133. doi:10.1111/j.1528-1167.2011. 03309.x Martinez A, Finegersh A, Cannon DM et al (2013) The 5-HT1A receptor and 5-HT transporter in temporal lobe epilepsy. Neurology 80:1465–1471. doi:10.1212/WNL.0b013e31828cf809 Storti SF, Galazzo BI, Del Felice A et al (2013) Combining ESI, ASL and PET for quantitative assessment of drug-resistant focal epilepsy. Neuroimage. doi:10.1016/j.neuroimage.2013.06.028 Theodore WH, Martinez AR, Khan OI et al (2012) PET of serotonin 1A receptors and cerebral glucose metabolism for temporal lobectomy. J Nucl Med 53:1375–1382. doi:10.2967/ jnumed.112.103093 Vivash L, Gregoire M-C, Lau EW et al (2013) 18F-flumazenil: a c-aminobutyric acid A-specific PET radiotracer for the localization of drug-resistant temporal lobe epilepsy. J Nucl Med 54:1270–1277. doi:10.2967/jnumed.112.107359 Chugani HT, Luat AF, Kumar A et al (2013) a-[11C]-Methyl-Ltryptophan–PET in 191 patients with tuberous sclerosis
complex. Neurology 81:674–680. doi:10.1212/WNL. 0b013e3182a08f3f Tiwari VN, Kumar A, Chakraborty PK, Chugani HT (2012) Can diffusion tensor imaging (DTI) identify epileptogenic tubers in tuberous sclerosis complex? Correlation with a-[11C]methyl-Ltryptophan ([11C] AMT) positron emission tomography (PET). J Child Neurol 27:598–603. doi:10.1177/0883073811422751 Butler T, Ichise M, Teich AF et al (2013) Imaging inflammation in a patient with epilepsy due to focal cortical dysplasia. J Neuroimaging 23:129–131. doi:10.1111/j.1552-6569.2010. 00572.x Wintermark P, Lechpammer M, Warfield SK et al (2013) Perfusion imaging of focal cortical dysplasia using arterial spin labeling: correlation with histopathological vascular density. J Child Neurol 28:1474–1482. doi:10.1177/0883073813488666 Stocchetti N (2014) Traumatic brain injury: problems and opportunities. Lancet Neurol 13:14–16. doi:10.1016/S14744422(13)70280-1 Hong YT, Veenith T, Dewar D et al (2014) Amyloid imaging with carbon 11-labeled Pittsburgh compound B for traumatic brain injury. JAMA Neurol 71:23–31. doi:10.1001/jamaneurol. 2013.4847 Malkki H (2014) Traumatic brain injury: PET imaging detects amyloid deposits after TBI. Nat Rev Neurol 10:3. doi:10.1038/ nrneurol.2013.250 Mitsis EM, Riggio S, Kostakoglu L et al (2014) Tauopathy PET and amyloid PET in the diagnosis of chronic traumatic encephalopathies: studies of a retired NFL player and of a man with FTD and a severe head injury. Transl Psychiatry 4:e441. doi:10.1038/tp.2014.91 Kawai N, Kawanishi M, Kudomi N et al (2013) Detection of brain amyloid b deposition in patients with neuropsychological impairment after traumatic brain injury: PET evaluation using Pittsburgh Compound-B. Brain Inj 27:1026–1031. doi:10.3109/ 02699052.2013.794963 Ramlackhansingh AF, Brooks DJ, Greenwood RJ et al (2011) Inflammation after trauma: microglial activation and traumatic brain injury. Ann Neurol 70:374–383 Iraji A, Benson RR, Welch RD et al (2014) Resting state functional connectivity in mild traumatic brain injury at the acute stage: independent component and seed based analyses. J Neurotrauma. doi:10.1089/neu.2014.3610 Nathan DE, Yeh PH, French LM et al (2014) Exploring variations in functional connectivity of the resting state default mode network in mild traumatic brain injury. Brain Connect. doi:10. 1089/brain.2014.0273 Abbas K, Shenk TE, Poole VN et al (2014) Alteration of default mode network in high school football athletes due to repetitive sub-concussive mTBI—a resting state fMRI study. Brain Connect. doi:10.1089/brain.2014.0279 Mishra AM, Bai X, Sanganahalli BG et al (2014) Decreased resting functional connectivity after traumatic brain injury in the rat. PLoS One 9:e95280. doi:10.1371/journal.pone.0095280 Ling JM, Klimaj S, Toulouse T, Mayer AR (2013) A prospective study of gray matter abnormalities in mild traumatic brain injury. Neurology 81:2121–2127. doi:10.1212/01.wnl.0000437 302.36064.b1 Eierud C, Craddock RC, Fletcher S et al (2014) Neuroimaging after mild traumatic brain injury: review and meta-analysis. Neuroimage Clin 4:283–294. doi:10.1016/j.nicl.2013.12.009 Yuh EL, Cooper SR, Mukherjee P et al (2014) Diffusion tensor imaging for outcome prediction in mild traumatic brain injury: a TRACK-TBI study. J Neurotrauma 31:1457–1477. doi:10.1089/ neu.2013.3171 Ilvesma¨ki T, Luoto TM, Hakulinen U et al (2014) Acute mild traumatic brain injury is not associated with white matter change
on diffusion tensor imaging. Brain 137:1876–1882. doi:10.1093/ brain/awu095 Fozouni N, Chopp M, Nejad-Davarani SP et al (2013) Characterizing brain structures and remodeling after TBI based on information content, diffusion entropy. PLoS One 8:e76343. doi:10.1371/journal.pone.0076343 Sharp DJ, Beckmann CF, Greenwood R et al (2011) Default mode network and structural connectivity after traumatic brain injury. Brain 134:2233–2247 Jilka SR, Scott G, Hamt T et al (2014) Damage to the salience network and interactions with the default mode network. J Neurosci 34:10798–10807 Politis M, Giannetti P, Su P et al (2012) Increased PK11195 PET binding in the cortex of patients with MS correlates with disability. Neurology 79:523–530. doi:10.1212/WNL.0b013e3182 635645 Giannetti P, Politis M, Su P et al (2014) Increased PK11195PET binding in normal-appearing white matter in clinically isolated syndrome. Brain. doi:10.1093/brain/awu331 Giannetti P, Politis M, Su P et al (2014) Microglia activation in multiple sclerosis black holes predicts outcome in progressive patients: an in vivo [(11)C](R)-PK11195-PET pilot study. Neurobiol Dis 65:203–210. doi:10.1016/j.nbd.2014.01.018 Oh U, Fujita M, Ikonomidou VN et al (2011) Translocator protein PET imaging for glial activation in multiple sclerosis. J Neuroimmune Pharmacol 6:354–361. doi:10.1007/s11481010-9243-6 Takano A, Piehl F, Hillert J et al (2013) In vivo TSPO imaging in patients with multiple sclerosis: a brain PET study with [18F]FEDAA1106. EJNMMI Res 3:30. doi:10.1186/2191219X-3-30 Colasanti A, Guo Q, Muhlert N et al (2014) In vivo assessment of brain white matter inflammation in multiple sclerosis with 18F-PBR111 PET. J Nucl Med 55:1112–1118. doi:10.2967/ jnumed.113.135129 Kiferle L, Politis M, Muraro PA, Piccini P (2011) Positron emission tomography imaging in multiple sclerosis-current status and future applications. Eur J Neurol 18:226–231. doi:10. 1111/j.1468-1331.2010.03154.x Stankoff B, Freeman L, Aigrot M-S et al (2011) Imaging central nervous system myelin by positron emission tomography in multiple sclerosis using [methyl-11C]-2-(40 -methylaminophenyl)-6-hydroxybenzothiazole. Ann Neurol 69:673–680. doi:10.1002/ana.22320 London˜o AC, Mora CA (2014) Nonconventional MRI biomarkers for in vivo monitoring of pathogenesis in multiple sclerosis. Neurol Neuroimmunol Neuroinflamm 1:e45. doi:10. 1212/NXI.0000000000000045 Klawiter EC (2013) Current and new directions in MRI in multiple sclerosis. Continuum (Minneap Minn) 19:1058–1073. doi:10.1212/01.CON.0000433283.00221.37 Deoni SCL, Rutt BK, Jones DK (2007) Investigating the effect of exchange and multicomponent T(1) relaxation on the short repetition time spoiled steady-state signal and the DESPOT1 T(1) quantification method. J Magn Reson Imaging 25:570–578. doi:10.1002/jmri.20836 Deoni SCL, Rutt BK, Jones DK (2008) Investigating exchange and multicomponent relaxation in fully-balanced steady-state free precession imaging. J Magn Reson Imaging 27:1421–1429. doi:10.1002/jmri.21079 Kitzler HH, Su J, Zeineh M et al (2012) Deficient MWF mapping in multiple sclerosis using 3D whole-brain multi-component relaxation MRI. Neuroimage 59:2670–2677. doi:10.1016/j. neuroimage.2011.08.052 Kearney H, Rocca MA, Valsasina P et al (2014) Magnetic resonance imaging correlates of physical disability in relapse
onset multiple sclerosis of long disease duration. Mult Scler 20:72–80. doi:10.1177/1352458513492245 Luo J, Yablonskiy DA, Hildebolt CF et al (2014) Gradient echo magnetic resonance imaging correlates with clinical measures and allows visualization of veins within multiple sclerosis lesions. Mult Scler 20:349–355. doi:10.1177/1352458513495935 Sbardella E, Tona F, Petsas N, Pantano P (2013) DTI measurements in multiple sclerosis: evaluation of brain damage and clinical implications. Mult Scler Int 2013:671730. doi:10.1155/ 2013/671730 Filippi M, Preziosa P, Pagani E et al (2013) Microstructural magnetic resonance imaging of cortical lesions in multiple sclerosis. Mult Scler 19:418–426. doi:10.1177/13524585 12457842 Harrison DM, Shiee N, Bazin P-L et al (2013) Tract-specific quantitative MRI better correlates with disability than conventional MRI in multiple sclerosis. J Neurol 260:397–406. doi:10. 1007/s00415-012-6638-8 Temel S, Keklikog˘lu HD, Keklig˘kog˘lu HD et al (2013) Diffusion tensor magnetic resonance imaging in patients with multiple sclerosis and its relationship with disability. Neuroradiol J 26:3–17
158. Stromillo ML, Giorgio A, Rossi F et al (2013) Brain metabolic changes suggestive of axonal damage in radiologically isolated syndrome. Neurology 80:2090–2094. doi:10.1212/WNL. 0b013e318295d707 159. Bertholdo D, Watcharakorn A, Castillo M (2013) Brain proton magnetic resonance spectroscopy: introduction and overview. Neuroimaging Clin N Am 23:359–380. doi:10.1016/j.nic.2012. 10.002 160. Rovira A, Alonso J (2013) 1H magnetic resonance spectroscopy in multiple sclerosis and related disorders. Neuroimaging Clin N Am 23:459–474. doi:10.1016/j.nic.2013.03.005 161. Llufriu S, Kornak J, Ratiney H et al (2014) Magnetic resonance spectroscopy markers of disease progression in multiple sclerosis. JAMA Neurol 71:840–847. doi:10.1001/jamaneurol.2014. 895 162. Choi I-Y, Lee S-P, Denney DR, Lynch SG (2011) Lower levels of glutathione in the brains of secondary progressive multiple sclerosis patients measured by 1H magnetic resonance chemical shift imaging at 3 T. Mult Scler 17:289–296. doi:10.1177/ 13524585103840