The power of neuroimaging biomarkers for screening frontotemporal dementia

Atrophy in behavioural variant frontotemporal dementia spans multiple large-scale prefrontal and temporal networks

The identification of a neurodegenerative disorder's distributed pattern of atrophy-or atrophy 'signature'-can lend insights into the cortical networks that degenerate in individuals with specific constellations of symptoms. In addition, this signature can be used as a biomarker to support early diagnoses and to potentially reveal pathological changes associated with said disorder. Here, we characterized the cortical atrophy signature of behavioural variant frontotemporal dementia (bvFTD). We used a data-driven approach to estimate cortical thickness using surface-based analyses in two independent, sporadic bvFTD samples (n = 30 and n = 71, total n = 101), using age- and gender-matched cognitively and behaviourally normal individuals. We found highly similar patterns of cortical atrophy across the two independent samples, supporting the reliability of our bvFTD signature. Next, we investigated whether our bvFTD signature targets specific large-scale cortical networks, as is the case for other neurodegenerative disorders. We specifically asked whether the bvFTD signature topographically overlaps with the salience network, as previous reports have suggested. We hypothesized that because phenotypic presentations of bvFTD are diverse, this would not be the case, and that the signature would cross canonical network boundaries. Consistent with our hypothesis, the bvFTD signature spanned rostral portions of multiple networks, including the default mode, limbic, frontoparietal control and salience networks. We then tested whether the signature comprised multiple anatomical subtypes, which themselves overlapped with specific networks. To explore this, we performed a hierarchical clustering analysis. This yielded three clusters, only one of which extensively overlapped with a canonical network (the limbic network). Taken together, these findings argue against the hypothesis that the salience network is preferentially affected in bvFTD, but rather suggest that-at least in patients who meet diagnostic criteria for the full-blown syndrome-neurodegeneration in bvFTD encompasses a distributed set of prefrontal, insular and anterior temporal nodes of multiple large-scale brain networks, in keeping with the phenotypic diversity of this disorder.

Association of blood pressure variability during acute care hospitalization and incident dementia

Background and objectives

Recognized as a potential risk factor for Alzheimer's disease and related dementias (ADRD), blood pressure variability (BPV) could be leveraged to facilitate identification of at-risk individuals at a population level. Granular BPV data are available during acute care hospitalization periods for potentially high-risk patients, but the incident ADRD risk association with BPV measured in this setting is unknown. Our objective was to evaluate the relation of BPV, measured during acute care hospitalization, and incidence of ADRD.

Methods

We retrospectively studied adults, without a prior ADRD diagnosis, who were admitted to a large quaternary care medical center in Southern California between January 1, 2013 and December 31, 2019. For all patients, determined BPV, calculated as variability independent of the mean (VIM), using blood pressure readings obtained as part of routine clinical care. We used multivariable Cox proportional hazards regression to examine the association between BP VIM during hospitalization and the development of incident dementia, determined by new ICD-9/10 coding or the new prescription of dementia medication, occurring at least 2 years after the index hospitalization.

Results

Of 81,892 adults hospitalized without a prior ADRD diagnosis, 2,442 (2.98%) went on to develop ADRD (2.6 to 5.2 years after hospitalization). In multivariable-adjusted Cox models, both systolic (HR 1.05, 95% CI 1.00-1.09) and diastolic (1.06, 1.02-1.10) VIM were associated with incident ADRD. In pre-specified stratified analyses, the VIM associations with incident ADRD were most pronounced in individuals over age 60 years and among those with renal disease or hypertension. Results were similar when repeated to include incident ADRD diagnoses made at least 1 or 3 years after index hospitalization.

Discussion

We found that measurements of BPV from acute care hospitalizations can be used to identify individuals at risk for developing a diagnosis of ADRD within approximately 5 years. Use of the readily accessible BPV measure may allow healthcare systems to risk stratify patients during periods of intense patient-provider interaction and, in turn, facilitate engagement in ADRD screening programs.

Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference

Characterizing a particular neurodegenerative condition against others possible diseases remains a challenge along clinical, biomarker, and neuroscientific levels. This is the particular case of frontotemporal dementia (FTD) variants, where their specific characterization requires high levels of expertise and multidisciplinary teams to subtly distinguish among similar physiopathological processes. Here, we used a computational approach of multimodal brain networks to address simultaneous multiclass classification of 298 subjects (one group against all others), including five FTD variants: behavioral variant FTD, corticobasal syndrome, nonfluent variant primary progressive aphasia, progressive supranuclear palsy, and semantic variant primary progressive aphasia, with healthy controls. Fourteen machine learning classifiers were trained with functional and structural connectivity metrics calculated through different methods. Due to the large number of variables, dimensionality was reduced, employing statistical comparisons and progressive elimination to assess feature stability under nested cross-validation. The machine learning performance was measured through the area under the receiver operating characteristic curves, reaching 0.81 on average, with a standard deviation of 0.09. Furthermore, the contributions of demographic and cognitive data were also assessed via multifeatured classifiers. An accurate simultaneous multiclass classification of each FTD variant against other variants and controls was obtained based on the selection of an optimum set of features. The classifiers incorporating the brain's network and cognitive assessment increased performance metrics. Multimodal classifiers evidenced specific variants' compromise, across modalities and methods through feature importance analysis. If replicated and validated, this approach may help to support clinical decision tools aimed to detect specific affectations in the context of overlapping diseases.

A Reproducible Deep-Learning-Based Computer-Aided Diagnosis Tool for Frontotemporal Dementia Using MONAI and Clinica Frameworks

Despite Artificial Intelligence (AI) being a leading technology in biomedical research, real-life implementation of AI-based Computer-Aided Diagnosis (CAD) tools into the clinical setting is still remote due to unstandardized practices during development. However, few or no attempts have been made to propose a reproducible CAD development workflow for 3D MRI data. In this paper, we present the development of an easily reproducible and reliable CAD tool using the Clinica and MONAI frameworks that were developed to introduce standardized practices in medical imaging. A Deep Learning (DL) algorithm was trained to detect frontotemporal dementia (FTD) on data from the NIFD database to ensure reproducibility. The DL model yielded 0.80 accuracy (95% confidence intervals: 0.64, 0.91), 1 sensitivity, 0.6 specificity, 0.83 F1-score, and 0.86 AUC, achieving a comparable performance with other FTD classification approaches. Explainable AI methods were applied to understand AI behavior and to identify regions of the images where the DL model misbehaves. Attention maps highlighted that its decision was driven by hallmarking brain areas for FTD and helped us to understand how to improve FTD detection. The proposed standardized methodology could be useful for benchmark comparison in FTD classification. AI-based CAD tools should be developed with the goal of standardizing pipelines, as varying pre-processing and training methods, along with the absence of model behavior explanations, negatively impact regulators' attitudes towards CAD. The adoption of common best practices for neuroimaging data analysis is a step toward fast evaluation of efficacy and safety of CAD and may accelerate the adoption of AI products in the healthcare system.

Ex vivo MRI and histopathology detect novel iron-rich cortical inflammation in frontotemporal lobar degeneration with tau versus TDP-43 pathology

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Comparative study of whole-hemisphere ex vivo T2*-weighted MRI and histopathology.

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Sample of FTLD-Tau and FTLD-TDP subtypes with reference to healthy and AD brain.

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Novel focal upper cortical-layer iron-rich pathology distinguishes FTLD-TDP from clinically-similar FTLD-Tau and AD.

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Distinct novel iron-rich FTLD-Tau pathology in mid-to-deep cortical-layers and WM.

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T2*-weighted MRI signatures offer in vivo biomarker targets for FTLD proteinopathy.

Frontotemporal lobar degeneration (FTLD) is a heterogeneous spectrum of age-associated neurodegenerative diseases that include two main pathologic categories of tau (FTLD-Tau) and TDP-43 (FTLD-TDP) proteinopathies. These distinct proteinopathies are often clinically indistinguishable during life, posing a major obstacle for diagnosis and emerging therapeutic trials tailored to disease-specific mechanisms. Moreover, MRI-derived measures have had limited success to date discriminating between FTLD-Tau or FTLD-TDP. T₂*-weighted (T2*w) ex vivo MRI has previously been shown to be sensitive to non-heme iron in healthy intracortical lamination and myelin, and to pathological iron deposits in amyloid-beta plaques and activated microglia in Alzheimer’s disease neuropathologic change (ADNC). However, an integrated, ex vivo MRI and histopathology approach is understudied in FTLD. We apply joint, whole-hemisphere ex vivo MRI at 7 T and histopathology to the study autopsy-confirmed FTLD-Tau (n = 4) and FTLD-TDP (n = 3), relative to ADNC disease-control brains with antemortem clinical symptoms of frontotemporal dementia (n = 2), and an age-matched healthy control. We detect distinct laminar patterns of novel iron-laden glial pathology in both FTLD-Tau and FTLD-TDP brains. We find iron-positive ameboid and hypertrophic microglia and astrocytes largely in deeper GM and adjacent WM in FTLD-Tau. In contrast, FTLD-TDP presents prominent superficial cortical layer iron reactivity in astrocytic processes enveloping small blood vessels with limited involvement of adjacent WM, as well as more diffuse distribution of punctate iron-rich dystrophic microglial processes across all GM lamina. This integrated MRI/histopathology approach reveals ex vivo MRI features that are consistent with these pathological observations distinguishing FTLD-Tau and FTLD-TDP subtypes, including prominent irregular hypointense signal in deeper cortex in FTLD-Tau whereas FTLD-TDP showed upper cortical layer hypointense bands and diffuse cortical speckling. Moreover, differences in adjacent WM degeneration and iron-rich gliosis on histology between FTLD-Tau and FTLD-TDP were also readily apparent on MRI as hyperintense signal and irregular areas of hypointensity, respectively that were more prominent in FTLD-Tau compared to FTLD-TDP. These unique histopathological and radiographic features were distinct from healthy control and ADNC brains, suggesting that iron-sensitive T2*w MRI, adapted to in vivo application at sufficient resolution, may eventually offer an opportunity to improve antemortem diagnosis of FTLD proteinopathies using tissue-validated methods.

The changing landscape of neuroimaging in frontotemporal lobar degeneration: from group-level observations to single-subject data interpretation

INTRODUCTION

While the imaging signatures of frontotemporal lobar degeneration (FTLD) phenotypes and genotypes are well-characterised based on group-level descriptive analyses, the meaningful interpretation of single MRI scans remains challenging. Single-subject MRI classification frameworks rely on complex computational models and large training datasets to categorise individual patients into diagnostic subgroups based on distinguishing imaging features. Reliable individual subject data interpretation is hugely important in the clinical setting to expedite the diagnosis and classify individuals into relevant prognostic categories.

AREAS COVERED

This article reviews (1) the neuroimaging studies that propose single-subject MRI classification strategies in symptomatic and pre-symptomatic FTLD, (2) potential practical implications and (3) the limitations of current single-subject data interpretation models.

EXPERT OPINION

Classification studies in FTLD have demonstrated the feasibility of categorising individual subjects into diagnostic groups based on multiparametric imaging data. Preliminary data indicate that pre-symptomatic FTLD mutation carriers may also be reliably distinguished from controls. Despite momentous advances in the field, significant further improvements are needed before these models can be developed into viable clinical applications.

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.

Isolation and characterization of antibody fragments selective for human FTD brain derived TDP-43 variants

BACKGROUND

Frontotemporal dementia (FTD) is the second leading cause of early onset dementia following Alzheimer's disease. It involves atrophy of the frontal and temporal regions of the brain affecting language, memory, and behavior. Transactive response DNA-binding protein 43 (TDP-43) pathology is found in most FTD and ALS cases. It plays a role in transcription, translation and serves as a shuttle between the nucleus and cytoplasm. Prior to its aggregation, TDP-43 exists as polyubiquitinated, hyperphosphorylated C-terminal fragments that correlate well with FTD disease progression. Because of the importance of TDP-43 in these diseases, reagents that can selectively recognize specific toxic TDP variants associated with onset and progression of FTD can be effective diagnostic and therapeutic tools.

RESULTS

We utilized a novel atomic force microscopy (AFM) based biopanning protocol to isolate single chain variable fragments (scFvs) from a phage display library that selectively bind TDP variants present in human FTD but not cognitively normal age matched brain tissue. We then used the scFvs (FTD-TDP1 through 5) to probe post-mortem brain tissue and sera samples for the presence of FTD related TDP variants. The scFvs readily selected the FTD tissue and sera samples over age matched controls. The scFvs were used in immunohistochemical analysis of FTD and control brain slices where the reagents showed strong staining with TDP in FTD brain tissue slice. FTD-TDP1, FTD-TDP2, FTD-TDP4 and FTD-TDP5 all protected neuronal cells against FTD TDP induced toxicity suggesting potential therapeutic value.

CONCLUSIONS

These results show existence of different disease specific TDP variants in FTD individuals. We have identified a panel of scFvs capable of recognizing these disease specific TDP variants in postmortem FTD tissue and sera samples over age matched controls and can thus serve as a biomarker tool.

Evaluating the reliability of neurocognitive biomarkers of neurodegenerative diseases across countries: A machine learning approach

Accurate early diagnosis of neurodegenerative diseases represents a growing challenge for current clinical practice. Promisingly, current tools can be complemented by computational decision-support methods to objectively analyze multidimensional measures and increase diagnostic confidence. Yet, widespread application of these tools cannot be recommended unless they are proven to perform consistently and reproducibly across samples from different countries. We implemented machine-learning algorithms to evaluate the prediction power of neurocognitive biomarkers (behavioral and imaging measures) for classifying two neurodegenerative conditions –Alzheimer Disease (AD) and behavioral variant frontotemporal dementia (bvFTD)– across three different countries (>200 participants). We use machine-learning tools integrating multimodal measures such as cognitive scores (executive functions and cognitive screening) and brain atrophy volume (voxel based morphometry from fronto-temporo-insular regions in bvFTD, and temporo-parietal regions in AD) to identify the most relevant features in predicting the incidence of the diseases. In the Country-1 cohort, predictions of AD and bvFTD became maximally improved upon inclusion of cognitive screenings outcomes combined with atrophy levels. Multimodal training data from this cohort allowed predicting both AD and bvFTD in the other two novel datasets from other countries with high accuracy (>90%), demonstrating the robustness of the approach as well as the differential specificity and reliability of behavioral and neural markers for each condition. In sum, this is the first study, across centers and countries, to validate the predictive power of cognitive signatures combined with atrophy levels for contrastive neurodegenerative conditions, validating a benchmark for future assessments of reliability and reproducibility.

Single Subject Classification of Alzheimer's Disease and Behavioral Variant Frontotemporal Dementia Using Anatomical, Diffusion Tensor, and Resting-State Functional Magnetic Resonance Imaging

BACKGROUND/OBJECTIVE

Overlapping clinical symptoms often complicate differential diagnosis between patients with Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). Magnetic resonance imaging (MRI) reveals disease specific structural and functional differences that aid in differentiating AD from bvFTD patients. However, the benefit of combining structural and functional connectivity measures to-on a subject-basis-differentiate these dementia-types is not yet known.

METHODS

Anatomical, diffusion tensor (DTI), and resting-state functional MRI (rs-fMRI) of 30 patients with early stage AD, 23 with bvFTD, and 35 control subjects were collected and used to calculate measures of structural and functional tissue status. All measures were used separately or selectively combined as predictors for training an elastic net regression classifier. Each classifier's ability to accurately distinguish dementia-types was quantified by calculating the area under the receiver operating characteristic curves (AUC).

RESULTS

Highest AUC values for AD and bvFTD discrimination were obtained when mean diffusivity, full correlations between rs-fMRI-derived independent components, and fractional anisotropy (FA) were combined (0.811). Similarly, combining gray matter density (GMD), FA, and rs-fMRI correlations resulted in highest AUC of 0.922 for control and bvFTD classifications. This, however, was not observed for control and AD differentiations. Classifications with GMD (0.940) and a GMD and DTI combination (0.941) resulted in similar AUC values (p = 0.41).

CONCLUSION

Combining functional and structural connectivity measures improve dementia-type differentiations and may contribute to more accurate and substantiated differential diagnosis of AD and bvFTD patients. Imaging protocols for differential diagnosis may benefit from also including DTI and rs-fMRI.

The pulvinar nucleus and antidepressant treatment: dynamic modeling of antidepressant response and remission with ultra-high field functional MRI

Functional magnetic resonance imaging (fMRI) successfully disentangled neuronal pathophysiology of major depression (MD), but only a few fMRI studies have investigated correlates and predictors of remission. Moreover, most studies have used clinical outcome parameters from two time points, which do not optimally depict differential response times. Therefore, we aimed to detect neuronal correlates of response and remission in an antidepressant treatment study with 7 T fMRI, potentially harnessing advances in detection power and spatial specificity. Moreover, we modeled outcome parameters from multiple study visits during a 12-week antidepressant fMRI study in 26 acute (aMD) patients compared to 36 stable remitted (rMD) patients and 33 healthy control subjects (HC). During an electrical painful stimulation task, significantly higher baseline activity in aMD compared to HC and rMD in the medial thalamic nuclei of the pulvinar was detected (p = 0.004, FWE-corrected), which was reduced by treatment. Moreover, clinical response followed a sigmoid function with a plateau phase in the beginning, a rapid decline and a further plateau at treatment end. By modeling the dynamic speed of response with fMRI-data, perigenual anterior cingulate activity after treatment was significantly associated with antidepressant response (p < 0.001, FWE-corrected). Temporoparietal junction (TPJ) baseline activity significantly predicted non-remission after 2 antidepressant trials (p = 0.005, FWE-corrected). The results underline the importance of the medial thalamus, attention networks in MD and antidepressant treatment. Moreover, by using a sigmoid model, this study provides a novel method to analyze the dynamic nature of response and remission for future trials.

Connectivity-based characterisation of subcortical grey matter pathology in frontotemporal dementia and ALS: a multimodal neuroimaging study

Frontotemporal dementia (FTD) phenotypes have distinctive and well-established cortical signatures, but their subcortical grey matter profiles are poorly characterised. The comprehensive characterisation of striatal and thalamic pathology along the ALS-FTD spectrum is particularly timely, as dysfunction of frontostriatal and cortico-thalamic networks contribute to phenotype-defining cognitive, behavioral, and motor deficits. Ten patients with behavioral-variant FTD, 11 patients with non-fluent-variant primary progressive aphasia, 5 patients with semantic-variant primary progressive aphasia, 14 ALS-FTD patients with C9orf72 hexanucleotide expansions, 12 ALS-FTD patients without hexanucleotide repeats, 36 ALS patients without cognitive impairment and 50 healthy controls were included in a prospective neuroimaging study. Striatal, thalamic, hippocampal and amygdala pathology was evaluated using volume measurements, density analyses and connectivity-based segmentation. Significant volume reductions were identified in the thalamus and putamen of non-fluent-variant PPA patients. Marked nucleus accumbens and hippocampal atrophy was observed in the behavioral-variant FTD cohort. Semantic-variant PPA patients only exhibited volumetric changes in the left hippocampus. C9-positive ALS-FTD patients showed preferential density reductions in thalamic sub-regions connected to motor and sensory cortical areas. C9-negative ALS-FTD patients exhibited striatal pathology in sub-regions projecting to rostral-motor and executive cortical areas. The bulk of striatal and thalamic pathology in non-fluent-variant PPA patients was identified in foci projecting to motor areas. Subcortical density alterations in svPPA patients were limited to basal ganglia regions with parietal projections. Striatal and thalamic changes in FTD exhibit selective, network-defined vulnerability patterns mirroring cortical pathology. Multi-modal cortico-basal imaging analyses confirm that the subcortical grey matter profiles of FTD phenotypes are just as distinct as their cortical signatures. Our findings support emerging concepts of network-wise degeneration, preferential circuit vulnerability and disease propagation along connectivity patterns.

Cortical microstructure in the behavioural variant of frontotemporal dementia: looking beyond atrophy

Cortical mean diffusivity has been proposed as a novel biomarker for the study of the cortical microstructure in Alzheimer's disease. In this multicentre study, we aimed to assess the cortical microstructural changes in the behavioural variant of frontotemporal dementia (bvFTD); and to correlate cortical mean diffusivity with clinical measures of disease severity and CSF biomarkers (neurofilament light and the soluble fraction beta of the amyloid precursor protein). We included 148 participants with a 3 T MRI and appropriate structural and diffusion weighted imaging sequences: 70 patients with bvFTD and 78 age-matched cognitively healthy controls. The modified frontotemporal lobar degeneration clinical dementia rating was obtained as a measure of disease severity. A subset of patients also underwent a lumbar puncture for CSF biomarker analysis. Two independent raters blind to the clinical data determined the presence of significant frontotemporal atrophy to dichotomize the participants into possible or probable bvFTD. Cortical thickness and cortical mean diffusivity were computed using a surface-based approach. We compared cortical thickness and cortical mean diffusivity between bvFTD (both using the whole sample and probable and possible bvFTD subgroups) and controls. Then we computed the Cohen's d effect size for both cortical thickness and cortical mean diffusivity. We also performed correlation analyses with the modified frontotemporal lobar degeneration clinical dementia rating score and CSF neuronal biomarkers. The cortical mean diffusivity maps, in the whole cohort and in the probable bvFTD subgroup, showed widespread areas with increased cortical mean diffusivity that partially overlapped with cortical thickness, but further expanded to other bvFTD-related regions. In the possible bvFTD subgroup, we found increased cortical mean diffusivity in frontotemporal regions, but only minimal loss of cortical thickness. The effect sizes of cortical mean diffusivity were notably higher than the effect sizes of cortical thickness in the areas that are typically involved in bvFTD. In the whole bvFTD group, both cortical mean diffusivity and cortical thickness correlated with measures of disease severity and CSF biomarkers. However, the areas of correlation with cortical mean diffusivity were more extensive. In the possible bvFTD subgroup, only cortical mean diffusivity correlated with the modified frontotemporal lobar degeneration clinical dementia rating. Our data suggest that cortical mean diffusivity could be a sensitive biomarker for the study of the neurodegeneration-related microstructural changes in bvFTD. Further longitudinal studies should determine the diagnostic and prognostic utility of this novel neuroimaging biomarker.

Single-subject classification of presymptomatic frontotemporal dementia mutation carriers using multimodal MRI

BACKGROUND

Classification models based on magnetic resonance imaging (MRI) may aid early diagnosis of frontotemporal dementia (FTD) but have only been applied in established FTD cases. Detection of FTD patients in earlier disease stages, such as presymptomatic mutation carriers, may further advance early diagnosis and treatment. In this study, we aim to distinguish presymptomatic FTD mutation carriers from controls on an individual level using multimodal MRI-based classification.

METHODS

Anatomical MRI, diffusion tensor imaging (DTI) and resting-state functional MRI data were collected in 55 presymptomatic FTD mutation carriers (8 microtubule-associated protein Tau, 35 progranulin, and 12 chromosome 9 open reading frame 72) and 48 familial controls. We calculated grey and white matter density features from anatomical MRI scans, diffusivity features from DTI, and functional connectivity features from resting-state functional MRI. These features were applied in a recently introduced multimodal behavioural variant FTD (bvFTD) classification model, and were subsequently used to train and test unimodal and multimodal carrier-control models. Classification performance was quantified using area under the receiver operator characteristic curves (AUC).

RESULTS

The bvFTD model was not able to separate presymptomatic carriers from controls beyond chance level (AUC = 0.582, p = 0.078). In contrast, one unimodal and several multimodal carrier-control models performed significantly better than chance level. The unimodal model included the radial diffusivity feature and had an AUC of 0.642 (p = 0.032). The best multimodal model combined radial diffusivity and white matter density features (AUC = 0.684, p = 0.004).

CONCLUSIONS

FTD mutation carriers can be separated from controls with a modest AUC even before symptom-onset, using a newly created carrier-control classification model, while this was not possible using a recent bvFTD classification model. A multimodal MRI-based classification score may therefore be a useful biomarker to aid earlier FTD diagnosis. The exclusive selection of white matter features in the best performing model suggests that the earliest FTD-related pathological processes occur in white matter.

Measurement Challenges in Research With Individuals With Cognitive Impairment

This State of the Science Commentary responds to the preliminary recommendation from the National Research Summit on Dementia Care, Services and Supports for Persons with Dementia and Their Caregivers to develop and identify a broad array of outcome measures (objective and subjective) that are meaningful to different stakeholders. Five significant measurement challenges that nurse scientists confront when conducting research with individuals with cognitive impairment are presented: (a) assessment of subjective memory complaints; (b) validity of self-report; (c) ecological validity of cognitive performance measures; (d) use of biomarkers (neuroimaging) for describing the biological dynamics of symptoms; and (e) effect of high variability in measurement on statistical significance. Methods for addressing these challenges are offered. [Res Gerontol Nurs. 2019; 12(1):7-15.].

Systems Biology Understanding of the Effects of Lithium on Affective and Neurodegenerative Disorders

Lithium has many widely varying biochemical and phenomenological effects, suggesting that a systems biology approach is required to understand its action. Multiple lines of evidence point to lithium intake and consequent blood levels as important determinants of incidence of neurodegenerative disease, showing that understanding lithium action is of high importance. In this paper we undertake first steps toward a systems approach by analyzing mutual enrichment between the interactomes of lithium-sensitive enzymes and the pathways associated with affective and neurodegenerative disorders. This work integrates information from two important databases, STRING and KEGG pathways. We find that for the majority of neurodegenerative disorders the mutual enrichment is many times greater than chance, reinforcing previous lines of evidence that lithium is an important influence on incidence of neurodegeneration. Our work suggests rational prioritization for which disorders are likely to be most sensitive to lithium and identifies genes that are likely to be useful targets for therapy adjunct to lithium.

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.

Single-subject classification of presymptomatic frontotemporal dementia mutation carriers using multimodal MRI

Background

Classification models based on magnetic resonance imaging (MRI) may aid early diagnosis of frontotemporal dementia (FTD) but have only been applied in established FTD cases. Detection of FTD patients in earlier disease stages, such as presymptomatic mutation carriers, may further advance early diagnosis and treatment. In this study, we aim to distinguish presymptomatic FTD mutation carriers from controls on an individual level using multimodal MRI-based classification.

Methods

Anatomical MRI, diffusion tensor imaging (DTI) and resting-state functional MRI data were collected in 55 presymptomatic FTD mutation carriers (8 microtubule-associated protein Tau, 35 progranulin, and 12 chromosome 9 open reading frame 72) and 48 familial controls. We calculated grey and white matter density features from anatomical MRI scans, diffusivity features from DTI, and functional connectivity features from resting-state functional MRI. These features were applied in a recently introduced multimodal behavioural variant FTD (bvFTD) classification model, and were subsequently used to train and test unimodal and multimodal carrier-control models. Classification performance was quantified using area under the receiver operator characteristic curves (AUC).

Results

The bvFTD model was not able to separate presymptomatic carriers from controls beyond chance level (AUC = 0.570, p = 0.11). In contrast, one unimodal and several multimodal carrier-control models performed significantly better than chance level. The unimodal model included the radial diffusivity feature and had an AUC of 0.646 (p = 0.021). The best multimodal model combined radial diffusivity and white matter density features (AUC = 0.680, p = 0.005).

Conclusions

FTD mutation carriers can be separated from controls with a modest AUC even before symptom-onset, using a newly created carrier-control classification model, while this was not possible using a recent bvFTD classification model. A multimodal MRI-based classification score may therefore be a useful biomarker to aid earlier FTD diagnosis. The exclusive selection of white matter features in the best performing model suggests that the earliest FTD-related pathological processes occur in white matter.

Longitudinal Diffusion Tensor Imaging Resembles Patterns of Pathology Progression in Behavioral Variant Frontotemporal Dementia (bvFTD)

Objective: Recently, the characteristic longitudinal distribution pattern of the underlying phosphorylated TDP-43 (pTDP-43) pathology in the behavioral variant of frontotemporal dementia (bvFTD) excluding Pick's disease (PiD) across specific brain regions was described. The aim of the present study was to investigate whether in vivo investigations of bvFTD patients by use of diffusion tensor imaging (DTI) were consistent with these proposed patterns of progression.

Methods: Sixty-two bvFTD patients and 47 controls underwent DTI in a multicenter study design. Of these, 49 bvFTD patients and 34 controls had a follow-up scan after ~12 months. Cross-sectional and longitudinal alterations were assessed by a two-fold analysis, i.e., voxelwise comparison of fractional anisotropy (FA) maps and a tract of interest-based (TOI) approach, which identifies tract structures that could be assigned to brain regions associated with disease progression.

Results: Whole brain-based spatial statistics showed white matter alterations predominantly in the frontal lobes cross-sectionally and longitudinally. The TOIs of bvFTD neuroimaging stages 1 and 2 (uncinate fascicle—bvFTD pattern I; corticostriatal pathway—bvFTD pattern II) showed highly significant differences between bvFTD patients and controls. The corticospinal tract-associated TOI (bvFTD pattern III) did not differ between groups, whereas the differences in the optic radiation (bvFTD pattern IV) reached significance. The findings in the corticospinal tract were due to a “dichotomous” behavior of FA changes there.

Conclusion: Longitudinal TOI analysis demonstrated a pattern of white matter pathways alterations consistent with patterns of pTDP-43 pathology.

A Longitudinal Study on Resting State Functional Connectivity in Behavioral Variant Frontotemporal Dementia and Alzheimer's Disease

BACKGROUND/OBJECTIVE

Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) are the most common types of early-onset dementia. We applied longitudinal resting state functional magnetic resonance imaging (fMRI) to delineate functional brain connections relevant for disease progression and diagnostic accuracy.

METHODS

We used two-center resting state fMRI data of 20 AD patients (65.1±8.0 years), 12 bvFTD patients (64.7±5.4 years), and 22 control subjects (63.8±5.0 years) at baseline and 1.8-year follow-up. We used whole-network and voxel-based network-to-region analyses to study group differences in functional connectivity at baseline and follow-up, and longitudinal changes in connectivity within and between groups.

RESULTS

At baseline, connectivity between paracingulate gyrus and executive control network, between cuneal cortex and medial visual network, and between paracingulate gyrus and salience network was higher in AD compared with controls. These differences were also present after 1.8 years. At follow-up, connectivity between angular gyrus and right frontoparietal network, and between paracingulate gyrus and default mode network was lower in bvFTD compared with controls, and lower compared with AD between anterior cingulate gyrus and executive control network, and between lateral occipital cortex and medial visual network. Over time, connectivity decreased in AD between precuneus and right frontoparietal network and in bvFTD between inferior frontal gyrus and left frontoparietal network. Longitudinal changes in connectivity between supramarginal gyrus and right frontoparietal network differ between both patient groups and controls.

CONCLUSION

We found disease-specific brain regions with longitudinal connectivity changes. This suggests the potential of longitudinal resting state fMRI to delineate regions relevant for disease progression and for diagnostic accuracy, although no group differences in longitudinal changes in the direct comparison of AD and bvFTD were found.

Towards a Unified Understanding of Lithium Action in Basic Biology and its Significance for Applied Biology

Lithium has literally been everywhere forever, since it is one of the three elements created in the Big Bang. Lithium concentration in rocks, soil, and fresh water is highly variable from place to place, and has varied widely in specific regions over evolutionary and geologic time. The biological effects of lithium are many and varied. Based on experiments in which animals are deprived of lithium, lithium is an essential nutrient. At the other extreme, at lithium ingestion sufficient to raise blood concentration significantly over 1 mM/, lithium is acutely toxic. There is no consensus regarding optimum levels of lithium intake for populations or individuals-with the single exception that lithium is a generally accepted first-line therapy for bipolar disorder, and specific dosage guidelines for sufferers of that condition are generally agreed on. Epidemiological evidence correlating various markers of social dysfunction and disease vs. lithium level in drinking water suggest benefits of moderately elevated lithium compared to average levels of lithium intake. In contrast to other biologically significant ions, lithium is unusual in not having its concentration in fluids of multicellular animals closely regulated. For hydrogen ions, sodium ions, potassium ions, calcium ions, chloride ions, and magnesium ions, blood and extracellular fluid concentrations are closely and necessarily regulated by systems of highly selective channels, and primary and secondary active transporters. Lithium, while having strong biological activity, is tolerated over body fluid concentrations ranging over many orders of magnitude. The lack of biological regulation of lithium appears due to lack of lithium-specific binding sites and selectivity filters. Rather lithium exerts its myriad physiological and biochemical effects by competing for macromolecular sites that are relatively specific for other cations, most especially for sodium and magnesium. This review will consider what is known about the nature of this competition and suggest using and extending this knowledge towards the goal of a unified understanding of lithium in biology and the application of that understanding in medicine and nutrition.

Tau PET imaging predicts cognition in atypical variants of Alzheimer's disease

Accumulation of paired helical filament tau contributes to neurodegeneration in Alzheimer's disease (AD). ¹⁸ F-flortaucipir is a positron emission tomography (PET) radioligand sensitive to tau in AD, but its clinical utility will depend in part on its ability to predict cognitive symptoms in diverse dementia phenotypes associated with selective, regional uptake. We examined associations between ¹⁸ F-flortaucipir and cognition in 14 mildly-impaired patients (12 with cerebrospinal fluid analytes consistent with AD pathology) who had amnestic (n = 5) and non-amnestic AD syndromes, including posterior cortical atrophy (PCA, n = 5) and logopenic-variant primary progressive aphasia (lvPPA, n = 4). Amnestic AD patients had deficits in memory; lvPPA in language; and both amnestic AD and PCA patients in visuospatial function. Associations with cognition were tested using sparse regression and compared to associations in anatomical regions-of-interest (ROIs). ¹⁸ F-flortaucipir uptake was expected to show regionally-specific correlations with each domain. In multivariate analyses, uptake was elevated in neocortical areas specifically associated with amnestic and non-amnestic syndromes. Uptake in left anterior superior temporal gyrus accounted for 67% of the variance in language performance. Uptake in right lingual gyrus predicted 85% of the variance in visuospatial performance. Memory was predicted by uptake in right fusiform gyrus and cuneus as well as a cluster comprising right anterior hippocampus and amygdala; this eigenvector explained 57% of the variance in patients' scores. These results provide converging evidence for associations between ¹⁸ F-flortaucipir uptake, tau pathology, and patients' cognitive symptoms.

Brain network efficiency is influenced by the pathologic source of corticobasal syndrome

Objective:

To apply network-based statistics to diffusion-weighted imaging tractography data and detect Alzheimer disease vs non-Alzheimer degeneration in the context of corticobasal syndrome.

Methods:

In a cross-sectional design, pathology was confirmed by autopsy or a pathologically validated CSF total tau-to-β-amyloid ratio (T-tau/Aβ). Using structural MRI data, we identify association areas in fronto-temporo-parietal cortex with reduced gray matter density in corticobasal syndrome (n = 40) relative to age-matched controls (n = 40). Using these fronto-temporo-parietal regions of interest, we construct structural brain networks in clinically similar subgroups of individuals with Alzheimer disease (n = 21) or non-Alzheimer pathology (n = 19) by linking these regions by the number of white matter streamlines identified in a deterministic tractography analysis of diffusion tensor imaging data. We characterize these structural networks using 5 graph-based statistics, and assess their relative utility in classifying underlying pathology with leave-one-out cross-validation using a supervised support vector machine.

Results:

Gray matter density poorly discriminates between Alzheimer disease and non-Alzheimer pathology subgroups with low sensitivity (57%) and specificity (52%). In contrast, a statistic of local network efficiency demonstrates very good discriminatory power, with 85% sensitivity and 84% specificity.

Conclusions:

Our results indicate that the underlying pathologic sources of corticobasal syndrome can be classified more accurately using graph theoretical statistics derived from patterns of white matter network organization in association cortex than by regional gray matter density alone. These results highlight the importance of a multimodal neuroimaging approach to diagnostic analyses of corticobasal syndrome.

Imaging and fluid biomarkers in frontotemporal dementia

Frontotemporal dementia (FTD), the second most common type of presenile dementia, is a heterogeneous neurodegenerative disease characterized by progressive behavioural and/or language problems, and includes a range of clinical, genetic and pathological subtypes. The diagnostic process is hampered by this heterogeneity, and correct diagnosis is becoming increasingly important to enable future clinical trials of disease-modifying treatments. Reliable biomarkers will enable us to better discriminate between FTD and other forms of dementia and to predict disease progression in the clinical setting. Given that different underlying pathologies probably require specific pharmacological interventions, robust biomarkers are essential for the selection of patients with specific FTD subtypes. This Review emphasizes the increasing availability and potential applications of structural and functional imaging biomarkers, and cerebrospinal fluid and blood fluid biomarkers in sporadic and genetic FTD. The relevance of new MRI modalities - such as voxel-based morphometry, diffusion tensor imaging and arterial spin labelling - in the early stages of FTD is discussed, together with the ability of these modalities to classify FTD subtypes. We highlight promising new fluid biomarkers for staging and monitoring of FTD, and underline the importance of large, multicentre studies of individuals with presymptomatic FTD. Harmonization in the collection and analysis of data across different centres is crucial for the implementation of new biomarkers in clinical practice, and will become a great challenge in the next few years.

Advances in neuroimaging in frontotemporal dementia

Frontotemporal dementia (FTD) is a clinically and neuroanatomically heterogeneous neurodegenerative disorder with multiple underlying genetic and pathological causes. Whilst initial neuroimaging studies highlighted the presence of frontal and temporal lobe atrophy or hypometabolism as the unifying feature in patients with FTD, more detailed studies have revealed diverse patterns across individuals, with variable frontal or temporal predominance, differing degrees of asymmetry, and the involvement of other cortical areas including the insula and cingulate, as well as subcortical structures such as the basal ganglia and thalamus. Recent advances in novel imaging modalities including diffusion tensor imaging, resting-state functional magnetic resonance imaging and molecular positron emission tomography imaging allow the possibility of investigating alterations in structural and functional connectivity and the visualisation of pathological protein deposition. This review will cover the major imaging modalities currently used in research and clinical practice, focusing on the key insights they have provided into FTD, including the onset and evolution of pathological changes and also importantly their utility as biomarkers for disease detection and staging, differential diagnosis and measurement of disease progression. Validating neuroimaging biomarkers that are able to accomplish these tasks will be crucial for the ultimate goal of powering upcoming clinical trials by correctly stratifying patient enrolment and providing sensitive markers for evaluating the effects and efficacy of disease-modifying therapies. This review describes the key insights provided by research into the major neuroimaging modalities currently used in research and clinical practice, including what they tell us about the onset and evolution of FTD and how they may be used as biomarkers for disease detection and staging, differential diagnosis and measurement of disease progression. This article is part of the Frontotemporal Dementia special issue.

Data-driven regions of interest for longitudinal change in three variants of frontotemporal lobar degeneration

INTRODUCTION

Longitudinal imaging of neurodegenerative disorders is a potentially powerful biomarker for use in clinical trials. In Alzheimer's disease, studies have demonstrated that empirically derived regions of interest (ROIs) can provide more reliable measurement of disease progression compared with anatomically defined ROIs.

METHODS

We set out to derive ROIs with optimal effect size for quantifying longitudinal change in a hypothetical clinical trial by comparing atrophy rates in 44 patients with behavioral variant of frontotemporal dementia (bvFTD), 30 with the semantic variant primary progressive aphasia (svPPA), and 26 with the nonfluent variant PPA (nfvPPA) to atrophy in 97 cognitively healthy controls.

RESULTS

The regions identified for each variant were generally what would be expected from prior studies of frontotemporal lobar degeneration (FTLD). Sample size estimates for detecting a 40% reduction in annual rate of ROI atrophy varied substantially across groups, being 103 per arm in bvFTD, 31 in nfvPPA, and 10 in svPPA, but in all groups were less than those estimated for a priori ROIs and clinical measures. The variability in location of peak regions of atrophy across individuals was highest in bvFTD and lowest in svPPA, likely relating to the differences in effect size.

CONCLUSIONS

These findings suggest that, while cross-validated maps of change can improve sensitivity to change in FTLD compared with a priori regions, the reliability of these maps differs considerably across syndromes. Future studies can utilize these maps to design clinical trials, and should try to identify factors accounting for the variability in patterns of atrophy across individuals, particularly those with bvFTD.

Multiparametric computer-aided differential diagnosis of Alzheimer's disease and frontotemporal dementia using structural and advanced MRI

Objectives

To investigate the added diagnostic value of arterial spin labelling (ASL) and diffusion tensor imaging (DTI) to structural MRI for computer-aided classification of Alzheimer's disease (AD), frontotemporal dementia (FTD), and controls.

Methods

This retrospective study used MRI data from 24 early-onset AD and 33 early-onset FTD patients and 34 controls (CN). Classification was based on voxel-wise feature maps derived from structural MRI, ASL, and DTI. Support vector machines (SVMs) were trained to classify AD versus CN (AD-CN), FTD-CN, AD-FTD, and AD-FTD-CN (multi-class). Classification performance was assessed by the area under the receiver-operating-characteristic curve (AUC) and accuracy. Using SVM significance maps, we analysed contributions of brain regions.

Results

Combining ASL and DTI with structural MRI resulted in higher classification performance for differential diagnosis of AD and FTD (AUC = 84%; p = 0.05) than using structural MRI by itself (AUC = 72%). The performance of ASL and DTI themselves did not improve over structural MRI. The classifications were driven by different brain regions for ASL and DTI than for structural MRI, suggesting complementary information.

Conclusions

ASL and DTI are promising additions to structural MRI for classification of early-onset AD, early-onset FTD, and controls, and may improve the computer-aided differential diagnosis on a single-subject level.

Key points

• Multiparametric MRI is promising for computer-aided diagnosis of early-onset AD and FTD.

• Diagnosis is driven by different brain regions when using different MRI methods.

• Combining structural MRI, ASL, and DTI may improve differential diagnosis of dementia.

Electronic supplementary material

The online version of this article (doi:10.1007/s00330-016-4691-x) contains supplementary material, which is available to authorized users.

Multimodal imaging evidence of pathology-mediated disease distribution in corticobasal syndrome

OBJECTIVE

To use multimodal neuroimaging to evaluate the influence of heterogeneous underlying pathology in corticobasal syndrome (CBS) on the neuroanatomical distribution of disease.

METHODS

We performed a retrospective evaluation of 35 patients with CBS with T1-weighted MRI, diffusion tensor imaging, and neuropathologic, genetic, or CSF evidence of underlying pathology. Patients were assigned to 2 groups: those with evidence of Alzheimer pathology (CBS-AD) and those without Alzheimer pathology (CBS-non-AD). Group comparisons of CBS-AD and CBS-non-AD assessed clinical features, gray matter (GM) cortical thickness, and white matter (WM) fractional anisotropy.

RESULTS

CBS-AD was found in 34% (n = 12) and CBS-non-AD in 66% (n = 23) of CBS patients. Clinical evaluations revealed that CBS-non-AD had a higher frequency of asymmetric rigidity compared to CBS-AD, but groups otherwise did not differ in dementia severity, impairments in cognition, or rates of extrapyramidal symptoms. We found frontoparietal GM and WM disease in each group compared to healthy, demographically comparable controls, as well as multimodal neuroimaging evidence of a double dissociation: CBS-non-AD had WM disease in the corpus callosum, corticospinal tract, and superior longitudinal fasciculus relative to CBS-AD, and CBS-AD had reduced temporoparietal GM relative to CBS-non-AD, including the precuneus and posterior cingulate.

CONCLUSIONS

Patients with CBS have a pathology-mediated dissociation of GM and WM disease. Multimodality neuroimaging may be useful for improving in vivo pathologic diagnosis of CBS.

Quantitative T2 mapping of white matter: applications for ageing and cognitive decline

In MRI, the coherence lifetime T2 is sensitive to the magnetic environment imposed by tissue microstructure and biochemistry in vivo. Here we explore the possibility that the use of T2 relaxometry may provide information complementary to that provided by diffusion tensor imaging (DTI) in ageing of healthy controls (HC), Alzheimer’s disease (AD) and mild cognitive impairment (MCI). T2 and diffusion MRI metrics were quantified in HC and patients with MCI and mild AD using multi-echo MRI and DTI. We used tract-based spatial statistics (TBSS) to evaluate quantitative MRI parameters in white matter (WM). A prolonged T2 in WM was associated with AD, and able to distinguish AD from MCI, and AD from HC. Shorter WM T2 was associated with better cognition and younger age in general. In no case was a reduction in T2 associated with poorer cognition. We also applied principal component analysis, showing that WM volume changes independently of  T2, MRI diffusion indices and cognitive performance indices. Our data add to the evidence that age-related and AD-related decline in cognition is in part attributable to WM tissue state, and much less to WM quantity. These observations suggest that WM is involved in AD pathology, and that T2 relaxometry is a potential imaging modality for detecting and characterising WM in cognitive decline and dementia.

Neuropsychological Testing Predicts Cerebrospinal Fluid Amyloid-β in Mild Cognitive Impairment

BACKGROUND

Psychometric tests predict conversion of mild cognitive impairment (MCI) to probable Alzheimer's disease (AD). Because the definition of clinical AD relies on those same psychometric tests, the ability of these tests to identify underlying AD pathology remains unclear.

OBJECTIVE

To determine the degree to which psychometric testing predicts molecular evidence of AD amyloid pathology, as indicated by cerebrospinal fluid (CSF) amyloid-β (Aβ)1 - 42, in patients with MCI, as compared to neuroimaging biomarkers.

METHODS

We identified 408 MCI subjects with CSF Aβ levels, psychometric test data, FDG-PET scans, and acceptable volumetric MR scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We used psychometric tests and imaging biomarkers in univariate and multivariate models to predict Aβ status.

RESULTS

The 30-min delayed recall score of the Rey Auditory Verbal Learning Test was the best predictor of Aβ status among the psychometric tests, achieving an AUC of 0.67 ± 0.02 and odds ratio of 2.5 ± 0.4. FDG-PET was the best imaging-based biomarker (AUC 0.67 ± 0.03, OR 3.2 ± 1.2), followed by hippocampal volume (AUC 0.64 ± 0.02, OR 2.4 ± 0.3). A multivariate analysis based on the psychometric tests improved on the univariate predictors, achieving an AUC of 0.68 ± 0.03 (OR 3.38 ± 1.2). Adding imaging biomarkers to the multivariate analysis did not improve the AUC.

CONCLUSION

Psychometric tests perform as well as imaging biomarkers to predict presence of molecular markers of AD pathology in MCI patients and should be considered in the determination of the likelihood that MCI is due to AD.

Magnetic Resonance Imaging to Detect Early Molecular and Cellular Changes in Alzheimer's Disease

Recent pharmaceutical trials have demonstrated that slowing or reversing pathology in Alzheimer's disease is likely to be possible only in the earliest stages of disease, perhaps even before significant symptoms develop. Pathology in Alzheimer's disease accumulates for well over a decade before symptoms are detected giving a large potential window of opportunity for intervention. It is therefore important that imaging techniques detect subtle changes in brain tissue before significant macroscopic brain atrophy. Current diagnostic techniques often do not permit early diagnosis or are too expensive for routine clinical use. Magnetic Resonance Imaging (MRI) is the most versatile, affordable, and powerful imaging modality currently available, being able to deliver detailed analyses of anatomy, tissue volumes, and tissue state. In this mini-review, we consider how MRI might detect patients at risk of future dementia in the early stages of pathological change when symptoms are mild. We consider the contributions made by the various modalities of MRI (structural, diffusion, perfusion, relaxometry) in identifying not just atrophy (a late-stage AD symptom) but more subtle changes reflective of early dementia pathology. The sensitivity of MRI not just to gross anatomy but to the underlying "health" at the cellular (and even molecular) scales, makes it very well suited to this task.

Frontotemporal Dementia and Psychiatric Illness: Emerging Clinical and Biological Links in Gene Carriers

OBJECTIVE

To describe psychiatric presentations in individuals with genetic mutations causing frontotemporal dementia (FTD).

DESIGN

Case descriptions from five carriers of FTD-related gene mutations with symptoms associated with non-neurodegenerative psychiatric disease.

SETTING

A comprehensive research program investigating genetic and non-genetic FTD at the University of California, San Francisco Memory and Aging Center.

PARTICIPANTS

Three proband and two non-proband gene carriers.

MEASUREMENTS

Medical history and neurological examination, neuropsychological testing, magnetic resonance and/or positron emission tomography imaging, and a genetic analysis to screen for dementia-related mutations. Genetic status was unknown at the time of initial evaluation.

RESULTS

The chosen cases are illustrative of the variety of presentations of psychiatric symptoms in FTD gene carriers. In some cases, a non-neurodegenerative psychiatric illness was diagnosed based on specific symptoms, but the diagnosis may have been inappropriate based on the overall syndrome. In other cases, symptoms closely resembling those seen in non-neurodegenerative psychiatric illness did occur, in some cases immediately preceding the development of dementia, and in other cases developing a decade prior to dementia symptoms.

CONCLUSIONS

Psychiatric symptoms in FTD gene carriers can be very similar to those seen in non-neurodegenerative psychiatric illness. Psychiatric symptoms with atypical features (e.g., late-life onset, insidiously worsening course) should prompt careful assessment for neurodegenerative disease. Guidelines for such an assessment should be established.

Resting state functional connectivity differences between behavioral variant frontotemporal dementia and Alzheimer's disease

INTRODUCTION

Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) are the most common types of early-onset dementia. Early differentiation between both types of dementia may be challenging due to heterogeneity and overlap of symptoms. Here, we apply resting state functional magnetic resonance imaging (fMRI) to study functional brain connectivity differences between AD and bvFTD.

METHODS

We used resting state fMRI data of 31 AD patients, 25 bvFTD patients, and 29 controls from two centers specialized in dementia. We studied functional connectivity throughout the entire brain, applying two different analysis techniques, studying network-to-region and region-to-region connectivity. A general linear model approach was used to study group differences, while controlling for physiological noise, age, gender, study center, and regional gray matter volume.

RESULTS

Given gray matter differences, we observed decreased network-to-region connectivity in bvFTD between (a) lateral visual cortical network and lateral occipital and cuneal cortex, and (b) auditory system network and angular gyrus. In AD, we found decreased network-to-region connectivity between the dorsal visual stream network and lateral occipital and parietal opercular cortex. Region-to-region connectivity was decreased in bvFTD between superior temporal gyrus and cuneal, supracalcarine, intracalcarine cortex, and lingual gyrus.

CONCLUSION

We showed that the pathophysiology of functional brain connectivity is different between AD and bvFTD. Our findings support the hypothesis that resting state fMRI shows disease-specific functional connectivity differences and is useful to elucidate the pathophysiology of AD and bvFTD. However, the group differences in functional connectivity are less abundant than has been shown in previous studies.

Multimodal imaging in Alzheimer's disease: validity and usefulness for early detection

Alzheimer's disease is a progressive neurodegenerative disease that typically manifests clinically as an isolated amnestic deficit that progresses to a characteristic dementia syndrome. Advances in neuroimaging research have enabled mapping of diverse molecular, functional, and structural aspects of Alzheimer's disease pathology in ever increasing temporal and regional detail. Accumulating evidence suggests that distinct types of imaging abnormalities related to Alzheimer's disease follow a consistent trajectory during pathogenesis of the disease, and that the first changes can be detected years before the disease manifests clinically. These findings have fuelled clinical interest in the use of specific imaging markers for Alzheimer's disease to predict future development of dementia in patients who are at risk. The potential clinical usefulness of single or multimodal imaging markers is being investigated in selected patient samples from clinical expert centres, but additional research is needed before these promising imaging markers can be successfully translated from research into clinical practice in routine care.

Frontotemporal lobar degeneration: defining phenotypic diversity through personalized medicine

Frontotemporal lobar degeneration (FTLD) comprises two main classes of neurodegenerative diseases characterized by neuronal/glial proteinaceous inclusions (i.e., proteinopathies) including tauopathies (i.e., FTLD-Tau) and TDP-43 proteinopathies (i.e., FTLD-TDP) while other very rare forms of FTLD are known such as FTLD with FUS pathology (FTLD-FUS). This review focuses mainly on FTLD-Tau and FLTD-TDP, which may present as several clinical syndromes: a behavioral/dysexecutive syndrome (behavioral variant frontotemporal dementia); language disorders (primary progressive aphasia variants); and motor disorders (amyotrophic lateral sclerosis, corticobasal syndrome, progressive supranuclear palsy syndrome). There is considerable heterogeneity in clinical presentations of underlying neuropathology and current clinical criteria do not reliably predict underlying proteinopathies ante-mortem. In contrast, molecular etiologies of hereditary FTLD are consistently associated with specific proteinopathies. These include MAPT mutations with FTLD-Tau and GRN, C9orf72, VCP and TARDBP with FTLD-TDP. The last decade has seen a rapid expansion in our knowledge of the molecular pathologies associated with this clinically and neuropathologically heterogeneous group of FTLD diseases. Moreover, in view of current limitations to reliably diagnose specific FTLD neuropathologies prior to autopsy, we summarize the current state of the science in FTLD biomarker research including neuroimaging, biofluid and genetic analyses. We propose that combining several of these biomarker modalities will improve diagnostic specificity in FTLD through a personalized medicine approach. The goals of these efforts are to enhance power for clinical trials focused on slowing or preventing progression of spread of tau, TDP-43 and other FTLD-associated pathologies and work toward the goal of defining clinical endophenotypes of FTD.

C9orf72 promoter hypermethylation is neuroprotective: Neuroimaging and neuropathologic evidence

OBJECTIVE

To use in vivo neuroimaging and postmortem neuropathologic analysis in C9orf72 repeat expansion patients to investigate the hypothesis that C9orf72 promoter hypermethylation is neuroprotective and regionally selective.

METHODS

Twenty patients with a C9orf72 repeat expansion participating in a high-resolution MRI scan and a clinical examination and a subset of patients (n = 11) were followed longitudinally with these measures. Gray matter (GM) density was related to C9orf72 promoter hypermethylation using permutation-based testing. Regional neuronal loss was measured in an independent autopsy series (n = 35) of C9orf72 repeat expansion patients.

RESULTS

GM analysis revealed that hippocampus, frontal cortex, and thalamus are associated with hypermethylation and thus appear to be relatively protected from mutant C9orf72. Neuropathologic analysis demonstrated an association between reduced neuronal loss and hypermethylation in hippocampus and frontal cortex. Longitudinal neuroimaging revealed that hypermethylation is associated with reduced longitudinal decline in GM regions protected by hypermethylation and longitudinal neuropsychological assessment demonstrated that longitudinal decline in verbal recall is protected by hypermethylation.

CONCLUSIONS

These cross-sectional and longitudinal neuroimaging studies, along with neuropathologic validation studies, provide converging evidence for neuroprotective properties of C9orf72 promoter hypermethylation. These findings converge with prior postmortem studies suggesting that C9orf72 promoter hypermethylation may be a neuroprotective target for drug discovery.

Differentiation between Subtypes of Primary Progressive Aphasia by Using Cortical Thickness and Diffusion-Tensor MR Imaging Measures

PURPOSE

To test a multimodal magnetic resonance (MR) imaging-based approach composed of cortical thickness and white matter (WM) damage metrics to discriminate between variants of primary progressive aphasia (PPA) that are nonfluent and/or agrammatic (NFVPPA) and semantic (SVPPA).

MATERIALS AND METHODS

This study was approved by the local ethics committees on human studies, and written informed consent from all patients was obtained before their enrollment. T1-weighted and diffusion-tensor (DT) MR images were obtained from 13 NFVPPA patients, 13 SVPPA patients, and 23 healthy control participants. Cortical thickness and DT MR imaging indices from the long-associative and interhemispheric WM tracts were obtained. A random forest (RF) analysis was used to identify the image features associated with each clinical syndrome. Individual patient classification was performed by using receiver operator characteristic curve analysis with cortical thickness, DT MR imaging, and a combination of the two modalities. RESULTS RF analysis showed that the best markers to differentiate the two PPA variants at an individual patient level among cortical thickness and DT MR imaging metrics were diffusivity abnormalities of the left inferior longitudinal and uncinate fasciculi and cortical thickness measures of the left temporal pole and inferior frontal gyrus. A combination of cortical thickness and DT MR imaging measures (the so-called gray-matter-and-WM model) was able to distinguish patients with NFVPPA and SVPPA with the following classification pattern: area under the curve, 0.91; accuracy, 0.89; sensitivity, 0.92; specificity, 0.85. Leave-one-out analysis demonstrated that the gray matter and WM model is more robust than the single MR modality models to distinguish PPA variants (accuracy was 0.86, 0.73, and 0.68 for the gray matter and WM model, the gray matter-only model, and the WM-only model, respectively).

CONCLUSION

A combination of structural and DT MR imaging metrics may provide a quantitative procedure to distinguish NFVPPA and SVPPA patients at an individual patient level. The discrimination accuracies obtained suggest that the gray matter and WM model is potentially relevant for the differential diagnosis of the PPA variants in clinical practice.

Eigenanatomy: sparse dimensionality reduction for multi-modal medical image analysis

Rigorous statistical analysis of multimodal imaging datasets is challenging. Mass-univariate methods for extracting correlations between image voxels and outcome measurements are not ideal for multimodal datasets, as they do not account for interactions between the different modalities. The extremely high dimensionality of medical images necessitates dimensionality reduction, such as principal component analysis (PCA) or independent component analysis (ICA). These dimensionality reduction techniques, however, consist of contributions from every region in the brain and are therefore difficult to interpret. Recent advances in sparse dimensionality reduction have enabled construction of a set of image regions that explain the variance of the images while still maintaining anatomical interpretability. The projections of the original data on the sparse eigenvectors, however, are highly collinear and therefore difficult to incorporate into multi-modal image analysis pipelines. We propose here a method for clustering sparse eigenvectors and selecting a subset of the eigenvectors to make interpretable predictions from a multi-modal dataset. Evaluation on a publicly available dataset shows that the proposed method outperforms PCA and ICA-based regressions while still maintaining anatomical meaning. To facilitate reproducibility, the complete dataset used and all source code is publicly available.

Biomarkers in the primary progressive aphasias

BACKGROUND

Primary progressive aphasia (PPA) is a progressive disorder of language that is increasingly recognised as an important presentation of a specific spectrum of neurodegenerative conditions.

AIMS

In an era of etiologically specific treatments for neurodegenerative conditions, it is crucial to establish the histopathologic basis for PPA. In this review, I discuss biomarkers for identifying the pathology underlying PPA.

MAIN CONTRIBUTION

Clinical syndromes suggest a probabilistic association between a specific PPA variant and an underlying pathology, but there are also many exceptions. A considerable body of work with biomarkers is now emerging as an important addition to clinical diagnosis. I review genetic, neuroimaging and biofluid studies that can help determine the pathologic basis for PPA.

CONCLUSIONS

Together with careful clinical examination, there is great promise that supplemental biomarker assessments will lead to accurate diagnosis of the pathology associated with PPA during life and serve as the basis for clinical trials in this spectrum of disease.

Relating brain anatomy and cognitive ability using a multivariate multimodal framework

Linking structural neuroimaging data from multiple modalities to cognitive performance is an important challenge for cognitive neuroscience. In this study we examined the relationship between verbal fluency performance and neuroanatomy in 54 patients with frontotemporal degeneration (FTD) and 15 age-matched controls, all of whom had T1- and diffusion-weighted imaging. Our goal was to incorporate measures of both gray matter (voxel-based cortical thickness) and white matter (fractional anisotropy) into a single statistical model that relates to behavioral performance. We first used eigenanatomy to define data-driven regions of interest (DD-ROIs) for both gray matter and white matter. Eigenanatomy is a multivariate dimensionality reduction approach that identifies spatially smooth, unsigned principal components that explain the maximal amount of variance across subjects. We then used a statistical model selection procedure to see which of these DD-ROIs best modeled performance on verbal fluency tasks hypothesized to rely on distinct components of a large-scale neural network that support language: category fluency requires a semantic-guided search and is hypothesized to rely primarily on temporal cortices that support lexical-semantic representations; letter-guided fluency requires a strategic mental search and is hypothesized to require executive resources to support a more demanding search process, which depends on prefrontal cortex in addition to temporal network components that support lexical representations. We observed that both types of verbal fluency performance are best described by a network that includes a combination of gray matter and white matter. For category fluency, the identified regions included bilateral temporal cortex and a white matter region including left inferior longitudinal fasciculus and frontal-occipital fasciculus. For letter fluency, a left temporal lobe region was also selected, and also regions of frontal cortex. These results are consistent with our hypothesized neuroanatomical models of language processing and its breakdown in FTD. We conclude that clustering the data with eigenanatomy before performing linear regression is a promising tool for multimodal data analysis.