Multiclass prediction of different dementia syndromes based on multi-centric volumetric MRI imaging

INTRODUCTION

Dementia syndromes can be difficult to diagnose. We aimed at building a classifier for multiple dementia syndromes using magnetic resonance imaging (MRI).

METHODS

Atlas-based volumetry was performed on T1-weighted MRI data of 426 patients and 51 controls from the multi-centric German Research Consortium of Frontotemporal Lobar Degeneration including patients with behavioral variant frontotemporal dementia, Alzheimer's disease, the three subtypes of primary progressive aphasia, i.e., semantic, logopenic and nonfluent-agrammatic variant, and the atypical parkinsonian syndromes progressive supranuclear palsy and corticobasal syndrome. Support vector machine classification was used to classify each patient group against controls (binary classification) and all seven diagnostic groups against each other in a multi-syndrome classifier (multiclass classification).

RESULTS

The binary classification models reached high prediction accuracies between 71 and 95% with a chance level of 50%. Feature importance reflected disease-specific atrophy patterns. The multi-syndrome model reached accuracies of more than three times higher than chance level but was far from 100%. Multi-syndrome model performance was not homogenous across dementia syndromes, with better performance in syndromes characterized by regionally specific atrophy patterns. Whereas diseases generally could be classified vs controls more correctly with increasing severity and duration, differentiation between diseases was optimal in disease-specific windows of severity and duration.

DISCUSSION

Results suggest that automated methods applied to MR imaging data can support physicians in diagnosis of dementia syndromes. It is particularly relevant for orphan diseases beside frequent syndromes such as Alzheimer's disease.

Characterization of the logopenic variant of Primary Progressive Aphasia: A systematic review and meta-analysis

The linguistic and anatomical variability of the logopenic variant of Primary Progressive Aphasia (lv-PPA) as defined by current diagnostic criteria has been the topic of an intense debate. The present review and meta-analysis aims at characterizing the profile of lv-PPA, by a comprehensive analysis of the available literature on the neuropsychological, neuroimaging, electrophysiological, pathological, and genetic features of lv-PPA. We conducted a systematic bibliographic search, leading to the inclusion of 207 papers. Of them, 12 were used for the Anatomical Likelihood Estimation meta-analysis on grey matter revealed by magnetic resonance imaging data. The results suggest that the current guidelines outline a relatively consistent syndrome, characterized by a core set of linguistic and, to a lesser extent, non-linguistic deficits, mirroring the involvement of left temporal and parietal regions typically affected by Alzheimer Disease pathology. Variations of the lv-PPA profile are discussed in terms of heterogeneity of the neuropsychological instruments and the diagnostic criteria adopted.

Association of Long-Term Speech Therapy and Neuromodulation in Primary Progressive Aphasia: Lessons from a Case Report

Primary progressive aphasia (PPA) is a neurodegenerative disorder with a progressive loss of language. Long-term support requires speech therapy but also individually set training programs. Here we propose an 8-month individualized speech-training program which alternates 3-week periods of transcranial direct current stimulation (tDCS) treatment with intensive daily language exercises and a 3-week period without tDCS treatment and a less intensive language exercise from home in a patient with non-fluent variant PPA (nfvPPA). The endpoints were the following: adherence to this program, language data after 8 months, questionnaires related to emotional valence, and brain volume changes. The results showed a persistent adherence after 8 months and a positive compliance reported by both the patient and the partner. The language evaluation showed a clinical stabilization. Moreover, a significant and positive influence of tDCS on mood was observed. This is, to our knowledge, the first ever published report of a combined neuromodulation and language training during the course of 8 months. Our finding suggests the feasibility of programs integrating hospital speech therapy, home training, and tDCS modulation in PPA. Further studies should be conducted in order to disentangle the contextual influences on language performance from the tDCS intervention effects and to address the observation of an initial improvement and a subsequent stabilization effect of language performances.

Data-driven staging of genetic frontotemporal dementia using multi-modal MRI

Frontotemporal dementia in genetic forms is highly heterogeneous and begins many years to prior symptom onset, complicating disease understanding and treatment development. Unifying methods to stage the disease during both the presymptomatic and symptomatic phases are needed for the development of clinical trials outcomes. Here we used the contrastive trajectory inference (cTI), an unsupervised machine learning algorithm that analyzes temporal patterns in high‐dimensional large‐scale population datasets to obtain individual scores of disease stage. We used cross‐sectional MRI data (gray matter density, T1/T2 ratio as a proxy for myelin content, resting‐state functional amplitude, gray matter fractional anisotropy, and mean diffusivity) from 383 gene carriers (269 presymptomatic and 115 symptomatic) and a control group of 253 noncarriers in the Genetic Frontotemporal Dementia Initiative. We compared the cTI‐obtained disease scores to the estimated years to onset (age—mean age of onset in relatives), clinical, and neuropsychological test scores. The cTI based disease scores were correlated with all clinical and neuropsychological tests (measuring behavioral symptoms, attention, memory, language, and executive functions), with the highest contribution coming from mean diffusivity. Mean cTI scores were higher in the presymptomatic carriers than controls, indicating that the method may capture subtle pre‐dementia cerebral changes, although this change was not replicated in a subset of subjects with complete data. This study provides a proof of concept that cTI can identify data‐driven disease stages in a heterogeneous sample combining different mutations and disease stages of genetic FTD using only MRI metrics.

Breakdown of specific functional brain networks in clinical variants of Alzheimer's disease

Alzheimer's disease (AD) is characterized by different clinical entities. Although AD phenotypes share a common molecular substrate (i.e., amyloid beta and tau accumulation), several clinicopathological differences exist. Brain functional networks might provide a macro-scale scaffolding to explain this heterogeneity. In this review, we summarize the evidence linking different large-scale functional network abnormalities to distinct AD phenotypes. Specifically, executive deficits in early-onset AD link with the dysfunction of networks that support sustained attention and executive functions. Posterior cortical atrophy relates to the breakdown of visual and dorsal attentional circuits, while the primary progressive aphasia variant of AD may be associated with the dysfunction of the left-lateralized language network. Additionally, network abnormalities might provide in vivo signatures for distinguishing proteinopathies that mimic AD, such as TAR DNA binding protein 43 related pathologies. These network differences vis-a-vis clinical syndromes are more evident in the earliest stage of AD. Finally, we discuss how these findings might pave the way for new tailored interventions targeting the most vulnerable brain circuit at the optimal time window to maximize clinical benefits.

Face Masks Protect From Infection but May Impair Social Cognition in Older Adults and People With Dementia

The coronavirus disease 2019 (COVID-19) pandemic will have a high impact on older adults and people with Alzheimer's disease and other dementias. Social cognition enables the understanding of another individual's feelings, intentions, desires and mental states, which is particularly important during the COVID-19 pandemic. To prevent further spread of the disease face masks have been recommended. Although justified for prevention of this potentially devastating disease, they partly cover the face and hamper emotion recognition and probably mindreading. As social cognition is already affected by aging and dementia, strategies must be developed to cope with these profound changes of communication. Face masking even could accelerate cognitive decline in the long run. Further studies are of uppermost importance to address face masks' impact on social cognition in aging and dementia, for instance by longitudinally investigating decline before and in the pandemic, and to design compensatory strategies. These issues are also relevant for face masking in general, such as in medical surroundings—beyond the COVID-19 pandemic.

Caregiver burden in patients with behavioural variant frontotemporal dementia and non-fluent variant and semantic variant primary progressive aphasia

Studies on caregiver burden in patients with frontotemporal lobar degeneration are rare, differ methodologically and show variable results. Single center longitudinal pilot study on caregiver burden and potential risk factors in patients with behavioural variant frontotemporal dementia (bvFTD) and semantic (svPPA) and non-fluent variants (nfvPPA) primary progressive aphasia. Forty-six bvFTD, nine svPPA, and six nfvPPA patients and caring relatives were analysed for up to 2 years using the Mini-Mental State Examination as global measure for cognitive performance, Frontal Assessment Battery (frontal lobe functions), Frontal Behavioural Inventory (personality and behaviour), Neuropsychiatric Inventory (dementia-related neuropsychiatric symptoms), Barthel Index and Lawton IADL Scale (basic and instrumental activities of daily living), the Caregiver Strain Index (CSI), and in most participants also the Zarit Burden Interview (ZBI). CSI baseline sum scores were highest in bvFTD (mean ± SD 5.5 ± 3.4, median 5, IQR 6), intermediate in svPPA (2.9 ± 2.3; 3; 3.5) and low in nfvPPA (1.6 ± 2.1; 1; 2). Similar differences of caregiver burden were found using the ZBI. During follow-up, CSI and ZBI sum scores deteriorated in svPPA, not in bvFTD and nfvPPA, and correlated significantly with personality and behaviour, neuropsychiatric symptoms, caregiver age, and instrumental, but not basic activities of daily living, Mini-Mental State Examination scores or frontal lobe functions. This study reveals differences in caregiver burden in variants of frontotemporal lobar degeneration. Caregivers should be systematically asked for caregiver burden from the time of the diagnosis to provide comprehensive support in time.

Different types of abstract concepts: evidence from two neurodegenerative patients

The observation of neurological patients showing selective impairments for specific conceptual categories contributed in the development of semantic memory theories. Here, we studied two patients (P01, P02), affected, respectively, by the semantic variant of Primary Progressive Aphasia (sv-PPA) and Cortico-Basal Syndrome (CBS). An implicit lexical decision task, including concrete (animals, tools) and abstract (emotions, social, quantity) concepts, was administered to patients and healthy controls.P01 and P02 showed an abolished priming effect for social and quantity-related concepts, respectively. This double dissociation suggests a role of different brain areas in representing specific abstract categories, giving insights for current semantic memory theories.

Quantitative comparison of subcortical and ventricular volumetry derived from MPRAGE and MP2RAGE images using different brain morphometry software

OBJECTIVE

In brain volume assessment with MR imaging, it is of interest to know the effects of the pulse sequence and software used, to determine whether they provide equivalent data. The aim of this study was to compare cross-sectional volumes of subcortical and ventricular structures and their repeatability derived from MP2RAGE and MPRAGE images using MorphoBox, and FIRST or ALVIN.

MATERIALS AND METHODS

MPRAGE and MP2RAGE T1-weighted images were obtained from 24 healthy volunteers. Back-to-back scans were performed in 12 of them. Volumes, coefficients of variation, concordance, and correlations were determined.

RESULTS

Significant differences were found for volumes derived from MorphoBox and FIRST. Ventricular volumes determined by MorphoBox and ALVIN were similar. Differences between volumes obtained using MPRAGE and MP2RAGE were significant for a few regions. Coefficients of variation, ranged from 0.2 to 9.1%, showed a significant inverse correlation with the mean volume. There was a correlation between volume measures, but agreement was rated as poor for most regions.

CONCLUSION

MP2RAGE sequences and MorphoBox are valid options for assessing subcortical and ventricular volumes, in the same way as MPRAGE and FIRST or ALVIN, accepted tools for clinical research. However, caution is needed when comparing volumes obtained with different tools.

Neuroimaging in Frontotemporal Lobar Degeneration: Research and Clinical Utility

Frontotemporal lobar dementia (FTLD) is a clinically and pathologically complex disease. Advances in neuroimaging techniques have provided a specialized set of tools to investigate underlying pathophysiology and identify clinical biomarkers that aid in diagnosis, prognostication, monitoring, and identification of appropriate endpoints in clinical trials. In this chapter, we review data discussing the utility of neuroimaging biomarkers in sporadic FTLD, with an emphasis on current and future clinical applications. Among those modalities readily utilized in clinical settings, T1-weighted structural magnetic resonance imaging (MRI) and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) are best supported in differential diagnosis and as targets for clinical trial endpoints. However, a number of nonclinical neuroimaging modalities, including diffusion tensor imaging and resting-state functional connectivity MRI, show promise as biomarkers to predict progression and as clinical trial endpoints. Other neuroimaging modalities, including amyloid PET, Tau PET, and arterial spin labeling MRI, are also discussed, though more work is required to establish their utility in FTLD in clinical settings.

Capgras Delusion in Posterior Cortical Atrophy-A Quantitative Multimodal Imaging Single Case Study

Although Alzheimer’s disease presents homogeneous histopathology, it causes several clinical phenotypes depending on brain regions involved. Beside the most abundant memory variant, several atypical variants exist. Among them posterior cortical atrophy (PCA) is associated with severe visuospatial/visuoperceptual deficits in the absence of significant primary ocular disease. Here, we report for the first time a case of Capgras delusion—a delusional misidentification syndrome, where patients think that familiar persons are replaced by identical “doubles” or an impostor—in a patient with PCA. The 57-year-old female patient was diagnosed with PCA and developed Capgras delusion 8 years after first symptoms. The patient did not recognize her husband, misidentified him as a stranger, and perceived him as a threat. Such misidentifications did not happen for other persons. Events could be interrupted by reassuring the husband’s identity by the patient’s female friend or children. We applied in-depth multimodal neuroimaging phenotyping and used single-subject voxel-based morphometry to identify atrophy changes specifically related to the development of the Capgras delusion. The latter, based on structural T1 magnetic resonance imaging, revealed progressive gray matter volume decline in occipital and temporoparietal areas, involving more the right than the left hemisphere, especially at the beginning. Correspondingly, the right fusiform gyrus was already affected by atrophy at baseline, whereas the left fusiform gyrus became involved in the further disease course. At baseline, glucose hypometabolism as measured by positron emission tomography (PET) with F18-fluorodesoxyglucose (FDG-PET) was evident in the parietooccipital cortex, more pronounced right-sided, and in the right frontotemporal cortex. Amyloid accumulation as assessed by PET with F18-florbetaben was found in the gray matter of the neocortex indicating underlying Alzheimer’s disease. Appearance of the Capgras delusion was related to atrophy in the right posterior cingulate gyrus/precuneus, as well as right middle frontal gyrus/frontal eye field, supporting right frontal areas as particularly relevant for Capgras delusion. Atrophy in these regions respectively might affect the default mode and dorsal attention networks as shown by meta-analytical co-activation and resting state functional connectivity analyses. This case elucidates the brain-behavior relationship in PCA and Capgras delusion.

Clinical value of cerebrospinal fluid neurofilament light chain in semantic dementia

BACKGROUND

Semantic dementia (SD) is a neurodegenerative disorder characterised by progressive language problems falling within the clinicopathological spectrum of frontotemporal lobar degeneration (FTLD). The development of disease-modifying agents may be facilitated by the relative clinical and pathological homogeneity of SD, but we need robust monitoring biomarkers to measure their efficacy. In different FTLD subtypes, neurofilament light chain (NfL) is a promising marker, therefore we investigated the utility of cerebrospinal fluid (CSF) NfL in SD.

METHODS

This large retrospective multicentre study compared cross-sectional CSF NfL levels of 162 patients with SD with 65 controls. CSF NfL levels of patients were correlated with clinical parameters (including survival), neuropsychological test scores and regional grey matter atrophy (including longitudinal data in a subset).

RESULTS

CSF NfL levels were significantly higher in patients with SD (median: 2326 pg/mL, IQR: 1628-3593) than in controls (577 (446-766), p<0.001). Higher CSF NfL levels were moderately associated with naming impairment as measured by the Boston Naming Test (rs =-0.32, p=0.002) and with smaller grey matter volume of the parahippocampal gyri (rs =-0.31, p=0.004). However, cross-sectional CSF NfL levels were not associated with progression of grey matter atrophy and did not predict survival.

CONCLUSION

CSF NfL is a promising biomarker in the diagnostic process of SD, although it has limited cross-sectional monitoring or prognostic abilities.

A human memory circuit derived from brain lesions causing amnesia

Human memory is thought to depend on a circuit of connected brain regions, but this hypothesis has not been directly tested. We derive a human memory circuit using 53 case reports of strokes causing amnesia and a map of the human connectome (n = 1000). This circuit is reproducible across discovery (n = 27) and replication (n = 26) cohorts and specific to lesions causing amnesia. Its hub is at the junction of the presubiculum and retrosplenial cortex. Connectivity with this single location defines a human brain circuit that incorporates > 95% of lesions causing amnesia. Lesion intersection with this circuit predicts memory scores in two independent datasets (N1 = 97, N2 = 176). This network aligns with neuroimaging correlates of episodic memory, abnormalities in Alzheimer's disease, and brain stimulation sites reported to enhance memory in humans.

Correlates of anomia in non-semantic variants of primary progressive aphasia converge over time

To track neural correlates of naming performance with disease progression, we estimated key areas affected in nonfluent/agrammatic (nfvPPA) and logopenic (lvPPA) primary progressive aphasia variants over time and changes in naming correlates over time. Twenty-nine non-semantic PPA participants (17 nfvPPA and 12 lvPPA) were selected based upon current diagnostic criteria and PiB-PET status and conducted a confrontation-naming task and a structural MRI. Linear mixed-effect models implemented in FreeSurfer were used for tracking cortical thickness and epicenters of atrophy over time. Using averaged cortical thickness of epicenters and naming performance as variables of interest, two sets of multivariate analyses were conducted to compare atrophy progression and naming correlates across groups. While all PPA participants demonstrated naming deterioration and progressive cortical thinning in the left temporal lobe and the left inferior frontal gyrus, the lvPPA cohort showed greater naming deterioration and thinning in the left posterior inferior parietal cortex over time than it did the nfvPPA cohort. The multivariate analyses confirmed a widespread cortical thinning in lvPPA over time, but a more rapid thinning in the right superior frontal gyrus of nfvPPA participants. Impaired naming correlated with common cortical regions in both groups. These regions included the left anterior superior temporal gyrus and the posterior middle temporal gyrus, which was primarily affected in lvPPA. Non-semantic PPA variants initially present with separate epicenters of atrophy and different spatial-temporal patterns of neurodegeneration over time, but the common involvement in key cortical regions of the left temporal lobe accounts for naming deterioration in both groups.

Atrophy in midbrain & cerebral/cerebellar pedunculi is characteristic for progressive supranuclear palsy - A double-validation whole-brain meta-analysis

OBJECTIVE

Progressive supranuclear palsy (PSP) is an atypical parkinsonian syndrome characterized by vertical gaze palsy and postural instability. Midbrain atrophy is suggested as a hallmark, but it has not been validated systematically in whole-brain imaging.

METHODS

We conducted whole-brain meta-analyses identifying disease-related atrophy in structural MRI. Eighteen studies were identified (N = 315 PSP, 393 controls) and separated into gray or white matter analyses (15/12). All patients were diagnosed according to the National Institute of Neurological Disorders and Stroke and the Society for PSP (NINDS-SPSP criteria, Litvan et al. (1996a)), which are now considered as PSP-Richardson syndrome (Höglinger et al., 2017). With overlay analyses, we double-validated two meta-analytical algorithms: anatomical likelihood estimation and seed-based D mapping. Additionally, we conducted region-of-interest effect size meta-analyses on radiological biomarkers and subtraction analyses differentiating PSP from Parkinson's disease.

RESULTS

Whole brain meta-analyses revealed consistent gray matter atrophy in bilateral thalamus, anterior insulae, midbrain, and left caudate nucleus. White matter alterations were consistently detected in bilateral superior/middle cerebellar pedunculi, cerebral pedunculi, and midbrain atrophy. Region-of-interest meta-analyses demonstrated that midbrain metrics generally perform very well in distinguishing PSP from other parkinsonian syndromes with strong effect sizes. Subtraction analyses identified the midbrain as differentiating between PSP and Parkinson's disease.

CONCLUSIONS

Our meta-analyses identify gray matter atrophy of the midbrain and white matter atrophy of the cerebral/cerebellar pedunculi and midbrain as characteristic for PSP. Results support the incorporation of structural MRI data, and particularly these structures, into the revised PSP diagnostic criteria.

FDG-PET hypometabolism is more sensitive than MRI atrophy in Parkinson's disease: A whole-brain multimodal imaging meta-analysis

Recently, revised diagnostic criteria for Parkinson's disease (PD) were introduced (Postuma et al., 2015). Yet, except for well-established dopaminergic imaging, validated imaging biomarkers for PD are still missing, though they could improve diagnostic accuracy.

We conducted systematic meta-analyses to identify PD-specific markers in whole-brain structural magnetic resonance imaging (MRI), [18F]-fluorodeoxyglucose-positron emission tomography (FDG-PET) and diffusion tensor imaging (DTI) studies. Overall, 74 studies were identified including 2323 patients and 1767 healthy controls. Studies were first grouped according to imaging modalities (MRI 50; PET 14; DTI 10) and then into subcohorts based on clinical phenotypes. To ensure reliable results, we combined established meta-analytical algorithms - anatomical likelihood estimation and seed-based D mapping - and cross-validated them in a conjunction analysis.

Glucose hypometabolism was found using FDG-PET extensively in bilateral inferior parietal cortex and left caudate nucleus with both meta-analytic methods. This hypometabolism pattern was confirmed in subcohort analyses and related to cognitive deficits (inferior parietal cortex) and motor symptoms (caudate nucleus). Structural MRI showed only small focal gray matter atrophy in the middle occipital gyrus that was not confirmed in subcohort analyses. DTI revealed fractional anisotropy reductions in the cingulate bundle near the orbital and anterior cingulate gyri in PD.

Our results suggest that FDG-PET reliably identifies consistent functional brain abnormalities in PD, whereas structural MRI and DTI show only focal alterations and rather inconsistent results. In conclusion, FDG-PET hypometabolism outperforms structural MRI in PD, although both imaging methods do not offer disease-specific imaging biomarkers for PD.

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Neuroimaging biomarkers could increase diagnostic accuracy in Parkinson's disease.

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Multimodal meta-analysis including whole-brain FDG-PET, MRI, and DTI

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Meta-analysis included 2323 Parkinson's disease patients and 1767 controls.

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FDG-PET functional changes are more consistent than structural changes in MRI-VBM /DTI.

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Glucose hypometabolism is detected in inferior parietal cortex/caudate nucleus.

Biomarkers for diseases with TDP-43 pathology

The discovery that aggregated transactive response DNA-binding protein 43 kDa (TDP-43) is the major component of pathological ubiquitinated inclusions in amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD) caused seminal progress in the unveiling of the genetic bases and molecular characteristics of these now so-called TDP-43 proteinopathies. Substantial increase in the knowledge of clinic-pathological coherencies, especially for FTLD variants, could be made in the last decade, but also revealed a considerable complexity of TDP-43 pathology and often a poor correlation of clinical and molecular disease characteristics. To date, an underlying TDP-43 pathology can be predicted only for patients with mutations in the genes C9orf72 and GRN, but is dependent on neuropathological verification in patients without family history, which represent the majority of cases. As etiology-specific therapies for neurodegenerative proteinopathies are emerging, methods to forecast TDP-43 pathology at patients' lifetime are highly required. Here, we review the current status of research pursued to identify specific indicators to predict or exclude TDP-43 pathology in the ALS-FTLD spectrum disorders and findings on candidates for prognosis and monitoring of disease progression in TDP-43 proteinopathies with a focus on TDP-43 with its pathological forms, neurochemical and imaging biomarkers.

Characterisation of "Positive" Behaviours in Primary Progressive Aphasias

BACKGROUND/AIMS

Although some patients with primary progressive aphasia (PPA) exhibit novel or improved skills after the onset of dementia, these changes have yet to be quantified. Therefore, this study systematically explored and identified the emergence of positive behaviours after dementia onset.

METHODS

This study included 48 carers of patients with PPA: 12 nonfluent/agrammatic PPA (nfvPPA), 22 semantic variant PPA (svPPA), and 14 logopenic variant PPA (lvPPA). The presence and frequency of positive behaviour changes after dementia onset were established using the Hypersensory and Social/Emotional Scale (HSS).

RESULTS

Scores on Sensitivity to Details, Visuospatial Activities, and Music Activities differed significantly among the groups. More specifically, svPPA was associated with increased visuospatial activity, but only in the mild stage of the disease; nfvPPA was associated with increased visuospatial activity and decreased music activity, while lvPPA exhibited the reverse profile.

CONCLUSIONS

The results demonstrate that subsets of PPA patients show novel or increased positive behaviours following dementia onset, and differences among subtypes may be helpful for improving diagnostic accuracy. Additionally, harnessing these skills may improve the quality of life of both patients and carers.

Morphometric MRI as a diagnostic biomarker of frontotemporal dementia: A systematic review to determine clinical applicability

Frontotemporal dementia (FTD) is difficult to diagnose, due to its heterogeneous nature and overlap in symptoms with primary psychiatric disorders. Brain MRI for atrophy is a key biomarker but lacks sensitivity in the early stage. Morphometric MRI-based measures and machine learning techniques are a promising tool to improve diagnostic accuracy. Our aim was to review the current state of the literature using morphometric MRI to classify FTD and assess its applicability for clinical practice. A search was completed using Pubmed and PsychInfo of studies which conducted a classification of subjects with FTD from non-FTD (controls or another disorder) using morphometric MRI metrics on an individual level, using single or combined approaches. 28 relevant articles were included and systematically reviewed following PRISMA guidelines. The studies were categorized based on the type of FTD subjects included and the group(s) against which they were classified. Studies varied considerably in subject selection, MRI methodology, and classification approach, and results are highly heterogeneous. Overall many studies indicate good diagnostic accuracy, with higher performance when differentiating FTD from controls (highest result was accuracy of 100%) than other dementias (highest result was AUC of 0.874). Very few machine learning algorithms have been tested in prospective replication. In conclusion, morphometric MRI with machine learning shows potential as an early diagnostic biomarker of FTD, however studies which use rigorous methodology and validate findings in an independent real-life cohort are necessary before this method can be recommended for use clinically.

A language-based sum score for the course and therapeutic intervention in primary progressive aphasia

Background

With upcoming therapeutic interventions for patients with primary progressive aphasia (PPA), instruments for the follow-up of patients are needed to describe disease progression and to evaluate potential therapeutic effects. So far, volumetric brain changes have been proposed as clinical endpoints in the literature, but cognitive scores are still lacking. This study followed disease progression predominantly in language-based performance within 1 year and defined a PPA sum score which can be used in therapeutic interventions.

Methods

We assessed 28 patients with nonfluent variant PPA, 17 with semantic variant PPA, 13 with logopenic variant PPA, and 28 healthy controls in detail for 1 year. The most informative neuropsychological assessments were combined to a sum score, and associations between brain atrophy were investigated followed by a sample size calculation for clinical trials.

Results

Significant absolute changes up to 20% in cognitive tests were found after 1 year. Semantic and phonemic word fluency, Boston Naming Test, Digit Span, Token Test, AAT Written language, and Cookie Test were identified as the best markers for disease progression. These tasks provide the basis of a new PPA sum score. Assuming a therapeutic effect of 50% reduction in cognitive decline for sample size calculations, a number of 56 cases is needed to find a significant treatment effect. Correlations between cognitive decline and atrophy showed a correlation up to r = 0.7 between the sum score and frontal structures, namely the superior and inferior frontal gyrus, as well as with left-sided subcortical structures.

Conclusion

Our findings support the high performance of the proposed sum score in the follow-up of PPA and recommend it as an outcome measure in intervention studies.

Electronic supplementary material

The online version of this article (10.1186/s13195-018-0345-3) contains supplementary material, which is available to authorized users.

Brain MRI Pattern Recognition Translated to Clinical Scenarios

We explored the performance of structure-based computational analysis in four neurodegenerative conditions [Ataxia (AT, n = 16), Huntington's Disease (HD, n = 52), Alzheimer's Disease (AD, n = 66), and Primary Progressive Aphasia (PPA, n = 50)], all characterized by brain atrophy. The independent variables were the volumes of 283 anatomical areas, derived from automated segmentation of T1-high resolution brain MRIs. The segmentation based volumetric quantification reduces image dimensionality from the voxel level [on the order of O(10⁶)] to anatomical structures [O(10²)] for subsequent statistical analysis. We evaluated the effectiveness of this approach on extracting anatomical features, already described by human experience and a priori biological knowledge, in specific scenarios: (1) when pathologies were relatively homogeneous, with evident image alterations (e.g., AT); (2) when the time course was highly correlated with the anatomical changes (e.g., HD), an analogy for prediction; (3) when the pathology embraced heterogeneous phenotypes (e.g., AD) so the classification was less efficient but, in compensation, anatomical and clinical information were less redundant; and (4) when the entity was composed of multiple subgroups that had some degree of anatomical representation (e.g., PPA), showing the potential of this method for the clustering of more homogeneous phenotypes that can be of clinical importance. Using the structure-based quantification and simple linear classifiers (partial least square), we achieve 87.5 and 73% of accuracy on differentiating AT and pre-symptomatic HD patents from controls, respectively. More importantly, the anatomical features automatically revealed by the classifiers agreed with the patterns previously described on these pathologies. The accuracy was lower (68%) on differentiating AD from controls, as AD does not display a clear anatomical phenotype. On the other hand, the method identified PPA clinical phenotypes and their respective anatomical signatures. Although most of the data are presented here as proof of concept in simulated clinical scenarios, structure-based analysis was potentially effective in characterizing phenotypes, retrieving relevant anatomical features, predicting prognosis, and aiding diagnosis, with the advantage of being easily translatable to clinics and understandable biologically.

Disentangling the neural correlates of corticobasal syndrome and corticobasal degeneration with systematic and quantitative ALE meta-analyses

Corticobasal degeneration is a scarce neurodegenerative disease, which can only be confirmed by histopathological examination. Reported to be associated with various clinical syndromes, its classical clinical phenotype is corticobasal syndrome. Due to the rareness of corticobasal syndrome/corticobasal degeneration and low numbers of patients included in single studies, meta-analyses are particularly suited to disentangle features of the clinical syndrome and histopathology. Using PubMed, we identified 11 magnetic resonance imaging studies measuring atrophy in 22 independent cohorts with 200 patients contrasted to 318 healthy controls. The anatomic likelihood estimation method was applied to reveal affected brain regions across studies. Corticobasal syndrome was related to gray matter loss in the basal ganglia/thalamus, frontal, parietal, and temporal lobes. In corticobasal degeneration patients, atrophy in the thalamus, frontal, temporal, and occipital lobes were found. Finally, in a conjunction analysis, the bilateral thalamus, the bilateral posterior frontomedian cortex, posterior midcingulate cortex and premotor area/supplementary motor area, and the left posterior superior and middle frontal gyrus/precentral gyrus were identified as areas associated with both, corticobasal syndrome and corticobasal degeneration. Remarkably, atrophy in the premotor area/supplementary motor area and posterior midcingulate/frontomedian cortex seems to be specific for corticobasal syndrome/corticobasal degeneration, whereas atrophy in the thalamus and the left posterior superior and middle frontal gyrus/precentral gyrus are also associated with other neurodegenerative diseases according to anatomic likelihood estimation method meta-analyses. Our study creates a new conceptual framework to understand, and distinguish between clinical features (corticobasal syndrome) and histopathological findings (corticobasal degeneration) by powerful data-driven meta-analytic approaches. Furthermore, it proposes regional-specific atrophy as an imaging biomarker for diagnosis of corticobasal syndrome/corticobasal degeneration ante-mortem.

Brain imaging could be used to distinguish between patients with corticobasal degeneration (CBD) and Parkinson's disease (PD). CBD is a rare condition caused by the gradual loss of brain cells in areas of the brain that link thinking to movement. The clinical features of CBD, referred to as corticobasal syndrome (CBS), are similar to those of patients with PD, but they progress differently. To aid earlier and more accurate diagnosis, Franziska Albrecht, at the Max Planck Institute for Human Cognitive and Brain Sciences, Germany, and colleagues reviewed 11 magnetic resonance imaging studies to find brain areas that are specifically affected in CBS/CBD patients. They show that cell loss in specific regions of the motor areas and frontomedian cortex is a hallmark of CBS/CBD, whereas cell loss in the thalamus and parts of the frontal/precentral gyrus were associated with other neurodegenerative diseases.

Neurofilament as a blood marker for diagnosis and monitoring of primary progressive aphasias

Objective:

To assess the utility of serum neurofilament for diagnosis and monitoring of primary progressive aphasia (PPA) variants.

Methods:

We investigated neurofilament light chain (NF-L) levels in blood of 99 patients with PPA (40 with nonfluent variant PPA [nfvPPA], 38 with semantic variant PPA [svPPA], 21 with logopenic variant PPA [lvPPA]) and compared diagnostic performance with that reached by CSF NF-L, phosphorylated neurofilament heavy chain (pNF-H), β-amyloid (Aβ1-42), tau, and phosphorylated tau. The longitudinal change of blood NF-L levels was measured and analyzed for correlation with functional decline and brain atrophy.

Results:

Serum NF-L is increased in PPA compared to controls and discriminates between nfvPPA/svPPA and lvPPA with 81% sensitivity and 67% specificity (cutoff 31 pg/mL). CSF NF-L, pNF-H, tau, phosphorylated tau, and Aβ1-42achieved similar performance, and pNF-H was the only marker for discrimination of nfvPPA from svPPA/lvPPA. In most patients with nfvPPA and svPPA, but not lvPPA, serum NF-L increased within follow-up. The increase correlated with functional decline and progression of atrophy of the left frontal lobe of all patients with PPAs and the right middle frontal gyrus of patients with nfvPPA and svPPA.

Conclusions:

Blood level of NF-L can aid the differential diagnosis of PPA variants, especially in combination with CSF pNF-H. Because serum NF-L correlates with functional decline and atrophy in the disease course, it qualifies as an objective disease status marker. Extended follow-up studies with cases of known neuropathology are imperative.

Classification of evidence:

This study provides Class I evidence that in patients with PPA, blood levels of NF-L can distinguish the logopenic variant from the nonfluent/agrammatic and semantic variants.

Predicting primary progressive aphasias with support vector machine approaches in structural MRI data

Primary progressive aphasia (PPA) encompasses the three subtypes nonfluent/agrammatic variant PPA, semantic variant PPA, and the logopenic variant PPA, which are characterized by distinct patterns of language difficulties and regional brain atrophy. To validate the potential of structural magnetic resonance imaging data for early individual diagnosis, we used support vector machine classification on grey matter density maps obtained by voxel-based morphometry analysis to discriminate PPA subtypes (44 patients: 16 nonfluent/agrammatic variant PPA, 17 semantic variant PPA, 11 logopenic variant PPA) from 20 healthy controls (matched for sample size, age, and gender) in the cohort of the multi-center study of the German consortium for frontotemporal lobar degeneration. Here, we compared a whole-brain with a meta-analysis-based disease-specific regions-of-interest approach for support vector machine classification. We also used support vector machine classification to discriminate the three PPA subtypes from each other. Whole brain support vector machine classification enabled a very high accuracy between 91 and 97% for identifying specific PPA subtypes vs. healthy controls, and 78/95% for the discrimination between semantic variant vs. nonfluent/agrammatic or logopenic PPA variants. Only for the discrimination between nonfluent/agrammatic and logopenic PPA variants accuracy was low with 55%. Interestingly, the regions that contributed the most to the support vector machine classification of patients corresponded largely to the regions that were atrophic in these patients as revealed by group comparisons. Although the whole brain approach took also into account regions that were not covered in the regions-of-interest approach, both approaches showed similar accuracies due to the disease-specificity of the selected networks. Conclusion, support vector machine classification of multi-center structural magnetic resonance imaging data enables prediction of PPA subtypes with a very high accuracy paving the road for its application in clinical settings.

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Aim was to evaluate the potential of multi-center MRI data for individual PPA diagnosis.

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We used support vector machine classification in PPA variants and healthy controls.

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We compared a whole brain approach with a ROI (taken from meta-analyses) approach.

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Accuracies were overall quite high, for both, the whole brain and the ROI approach.

Atrophy and structural covariance of the cholinergic basal forebrain in primary progressive aphasia

Primary progressive aphasia (PPA) is characterized by profound destruction of cortical language areas. Anatomical studies suggest an involvement of cholinergic basal forebrain (BF) in PPA syndromes, particularly in the area of the nucleus subputaminalis (NSP). Here we aimed to determine the pattern of atrophy and structural covariance as a proxy of structural connectivity of BF nuclei in PPA variants. We studied 62 prospectively recruited cases with the clinical diagnosis of PPA and 31 healthy older control participants from the cohort study of the German consortium for frontotemporal lobar degeneration (FTLD). We determined cortical and BF atrophy based on high-resolution magnetic resonance imaging (MRI) scans. Patterns of structural covariance of BF with cortical regions were determined using voxel-based partial least square analysis. We found significant atrophy of total BF and BF subregions in PPA patients compared with controls [F(1, 82) = 20.2, p < .001]. Atrophy was most pronounced in the NSP and the posterior BF, and most severe in the semantic variant and the nonfluent variant of PPA. Structural covariance analysis in healthy controls revealed associations of the BF nuclei, particularly the NSP, with left hemispheric predominant prefrontal, lateral temporal, and parietal cortical areas, including Broca's speech area (p < .001, permutation test). In contrast, the PPA patients showed preserved structural covariance of the BF nuclei mostly with right but not with left hemispheric cortical areas (p < .001, permutation test). Our findings agree with the neuroanatomically proposed involvement of the cholinergic BF, particularly the NSP, in PPA syndromes. We found a shift from a structural covariance of the BF with left hemispheric cortical areas in healthy aging towards right hemispheric cortical areas in PPA, possibly reflecting a consequence of the profound and early destruction of cortical language areas in PPA.