Differences in structural covariance brain networks between behavioral variant frontotemporal dementia and Alzheimer's disease

Cognitive impairment in neurodegenerative diseases: A trans-diagnostic approach using a lesion-symptom mapping analysis

INTRODUCTION

Neurodegenerative disorders, such as Alzheimer's disease (AD) and frontotemporal dementia (bvFTD), reflect a spectrum of cognitive impairments unified by cognitive decline. Traditional diagnostic approaches often overlook shared landscapes of these disorders. A transdiagnostic approach, cutting across conventional boundaries, may improve understanding of shared mechanisms. This study uses lesion-symptom mapping (LSM) to identify critical brain structures responsible for cognitive impairments.

METHODS

Patients diagnosed with Mild Cognitive Impairment (MCI), probable AD, and probable bvFTD were recruited from our memory clinic. Diagnoses were made by a multidisciplinary team using established criteria. Participants underwent detailed medical and neurological examinations, neuroimaging, cerebrospinal fluid analysis, and neuropsychological assessment. MRI scans were processed using FreeSurfer. LSM was used to assess correlations between brain structures and cognitive performance.

RESULTS

Significant correlations were found between neuropsychological test scores and reduced volume in specific brain regions. The Free and Cued Selective Reminding Test was linked to the right hippocampus and left nucleus accumbens. The Brief Visuospatial Memory Test-Revised correlated with the right hippocampus, left nucleus accumbens, and right middle temporal gyrus. Verbal fluency was linked to the left superior temporal sulcus and left middle temporal gyrus. Digit Span forward correlated with left superior frontal gyrus and left inferior parietal region, while Digit Span backward was linked to the right precuneus. Digit-Symbol Coding was associated with the left inferior parietal region.

CONCLUSIONS

This study highlights common neural targets in MCI, AD, and bvFTD and their link with cognitive impairment, emphasizing the value of LSM within a transdiagnostic approach to neurodegenerative diseases.

A computational ontology framework for the synthesis of multi-level pathology reports from brain MRI scans

BackgroundConvolutional neural network (CNN) based volumetry of MRI data can help differentiate Alzheimer's disease (AD) and the behavioral variant of frontotemporal dementia (bvFTD) as causes of cognitive decline and dementia. However, existing CNN-based MRI volumetry tools lack a structured hierarchical representation of brain anatomy, which would allow for aggregating regional pathological information and automated computational inference.ObjectiveDevelop a computational ontology pipeline for quantifying hierarchical pathological abnormalities and visualize summary charts for brain atrophy findings, aiding differential diagnosis.MethodsUsing FastSurfer, we segmented brain regions and measured volume and cortical thickness from MRI scans pooled across multiple cohorts (N = 3433; ADNI, AIBL, DELCODE, DESCRIBE, EDSD, and NIFD), including healthy controls, prodromal and clinical AD cases, and bvFTD cases. Employing the Web Ontology Language (OWL), we built a semantic model encoding hierarchical anatomical information. Additionally, we created summary visualizations based on sunburst plots for visual inspection of the information stored in the ontology.ResultsOur computational framework dynamically estimated and aggregated regional pathological deviations across different levels of neuroanatomy abstraction. The disease similarity index derived from the volumetric and cortical thickness deviations achieved an AUC of 0.88 for separating AD and bvFTD, which was also reflected by distinct atrophy profile visualizations.ConclusionsThe proposed automated pipeline facilitates visual comparison of atrophy profiles across various disease types and stages. It provides a generalizable computational framework for summarizing pathologic findings, potentially enhancing the physicians' ability to evaluate brain pathologies robustly and interpretably.

Radiomics feature similarity: A novel approach for characterizing brain network changes in patients with behavioral variant frontotemporal dementia

INTRODUCTION

Network modeling is increasingly used to study brain alterations in neurological disorders. In this study, we apply a novel modeling approach based on the similarity of regional radiomics feature to characterize gray matter network changes in patients with behavioral variant frontotemporal dementia (bvFTD) using MRI data.

METHODS

In this cross-sectional study, we assessed structural 3 T MRI data from twenty patients with bvFTD and 20 cognitively normal controls. Radiomics features were extracted from T1-weighted MRI based on cortical and subcortical brain segmentation. Similarity in radiomics features between brain regions was used to construct intra-individual structural gray matter networks. Regional mean connectivity strength (RMCS) and region-to-region radiomics similarity were compared between bvFTD patients and controls. Finally, associations between network measures, clinical data, and biological features were explored in bvFTD patients.

RESULTS

Relative to controls, patients with bvFTD showed higher RMCS values in the superior frontal gyrus, right inferior temporal gyrus and right inferior parietal gyrus (FDR-corrected p < 0.05). Patients with bvFTD also showed several edges of increased radiomics similarity in key components of the frontal, temporal, parietal and thalamic pathways compared to controls (FDR-corrected p < 0.05). Network measures in frontotemporal circuits were associated with Mini-Mental State Examination scores and cerebrospinal fluid total-tau protein levels (Spearman r > |0.7|, p < 0.005).

CONCLUSIONS

Our study provides new insights into frontotemporal network changes associated with bvFTD, highlighting specific associations between network measures and clinical/biological features. Radiomics feature similarity analysis could represent a useful approach for characterizing brain changes in patients with frontotemporal dementia.

Multi-modal machine learning approach for early detection of neurodegenerative diseases leveraging brain MRI and wearable sensor data

Neurodegenerative diseases, such as Alzheimer's and Parkinson's Disease, pose a significant healthcare burden to the aging population. Structural MRI brain parameters and accelerometry data from wearable devices have been proven to be useful predictors for these diseases but have been separately examined in the prior literature. This study aims to determine whether a combination of accelerometry data and MRI brain parameters may improve the detection and prognostication of Alzheimer's and Parkinson's disease, compared with MRI brain parameters alone. A cohort of 19,793 participants free of neurodegenerative disease at the time of imaging and accelerometry data capture from the UK Biobank with longitudinal follow-up was derived to test this hypothesis. Relevant structural MRI brain parameters, accelerometry data collected from wearable devices, standard polygenic risk scores and lifestyle information were obtained. Subsequent development of neurodegenerative diseases among participants was recorded (mean follow-up time of 5.9 years), with positive cases defined as those diagnosed at least one year after imaging. A machine learning algorithm (XGBoost) was employed to create prediction models for the development of neurodegenerative disease. A prediction model consisting of all factors, including structural MRI brain parameters, accelerometry data, PRS, and lifestyle information, achieved the highest AUC value (0.819) out of all tested models. A model that excluded MRI brain parameters achieved the lowest AUC value (0.688). Feature importance analyses revealed 18 out of 20 most important features were structural MRI brain parameters, while 2 were derived from accelerometry data. Our study demonstrates the potential utility of combining structural MRI brain parameters with accelerometry data from wearable devices to predict the incidence of neurodegenerative diseases. Future prospective studies across different populations should be conducted to confirm these study results and look for differences in predictive ability for various types of neurodegenerative diseases.

Network Biomarkers of Alzheimer's Disease Risk Derived from Joint Volume and Texture Covariance Patterns in Mouse Models

Alzheimer's disease (AD) lacks effective cures and is typically detected after substantial pathological changes have occurred, making intervention challenging. Early detection and understanding of risk factors and their downstream effects are therefore crucial. Animal models provide valuable tools to study these prodromal stages. We investigated various levels of genetic risk for AD using mice expressing the three major human APOE alleles in place of mouse APOE. We leverage these mouse models utilizing high-resolution magnetic resonance diffusion imaging, due to its ability to provide multiple parameters that can be analysed jointly. We examine how APOE genotype interacts with age, sex, diet, and immunity to yield jointly discernable changes in regional brain volume and fractional anisotropy, a sensitive metric for brain water diffusion. Our results demonstrate that genotype strongly influences the caudate putamen, pons, cingulate cortex, and cerebellum, while sex affects the amygdala and piriform cortex bilaterally. Immune status impacts numerous regions, including the parietal association cortices, thalamus, auditory cortex, V1, and bilateral dentate cerebellar nuclei. Risk factor interactions particularly affect the amygdala, thalamus, and pons. APOE2 mice on a regular diet exhibited the fewest temporal changes, suggesting resilience, while APOE3 mice showed minimal effects from a high-fat diet (HFD). HFD amplified aging effects across multiple brain regions. The interaction of AD risk factors, including diet, revealed significant changes in the periaqueductal gray, pons, amygdala, inferior colliculus, M1, and ventral orbital cortex. Future studies should investigate the mechanisms underlying these coordinated changes in volume and texture, potentially by examining network similarities in gene expression and metabolism, and their relationship to structural pathways involved in neurodegenerative disease progression.

Increased structural covariance of cortical measures in individuals with an at-risk mental state

An anomalous pattern of structural covariance has been reported in schizophrenia, which has been suggested to represent connectome changes during brain maturation and neuroprogressive processes. It remains unclear whether similar differences exist in a clinical high-risk state for psychosis, and if they are associated with a prodromal phenotype and/or later psychosis onset. This multicenter magnetic resonance imaging study cross-sectionally examined structural covariance in a large at-risk mental state (ARMS) sample with different outcomes. The whole-brain structural covariance of four cortical measures (thickness, area, volume, and gyrification) was assessed in 155 individuals with ARMS, who were subclassified into 26 (16.8 %) with a later psychosis onset (ARMS-P), 44 with persistent subthreshold psychotic symptoms, and 53 with the remission of psychotic symptoms (ARMS-R) during the clinical follow-up, and 191 healthy controls. The relationships of changes in structural covariance with clinical symptoms and cognitive impairments were also investigated in the ARMS subsample. Structural covariance was significantly higher in widespread cortical regions in the ARMS group than in the controls, with each cortical measure having a different pattern in affected cortical regions. The higher structural covariance of the cortical area was partly related to severe suspiciousness-persecutory ideation. Structural covariance was significantly higher, mainly in fronto-parietal gyrification, in the ARMS-P group than in the ARMS-R group. The present results suggest that changes in structural covariance result in psychosis vulnerability and the excessive structural covariance of brain gyrification in ARMS subjects may contribute to their later clinical course.

The brain network hub degeneration in Alzheimer's disease

Alzheimer's disease (AD) has been conceptualized as a syndrome of brain network dysfunction. Recent imaging connectomics studies have provided unprecedented opportunities to map structural and functional brain networks in AD. By reviewing molecular, imaging, and computational modeling studies, we have shown that highly connected brain hubs are primarily distributed in the medial and lateral prefrontal, parietal, and temporal regions in healthy individuals and that the hubs are selectively and severely affected in AD as manifested by increased amyloid-beta deposition and regional atrophy, hypo-metabolism, and connectivity dysfunction. Furthermore, AD-related hub degeneration depends on the imaging modality with the most notable degeneration in the medial temporal hubs for morphological covariance networks, the prefrontal hubs for structural white matter networks, and in the medial parietal hubs for functional networks. Finally, the AD-related hub degeneration shows metabolic, molecular, and genetic correlates. Collectively, we conclude that the brain-network-hub-degeneration framework is promising to elucidate the biological mechanisms of network dysfunction in AD, which provides valuable information on potential diagnostic biomarkers and promising therapeutic targets for the disease.

Associations of conservatism and jumping to conclusions biases with aberrant salience and default mode network

AIM

While conservatism bias refers to the human need for more evidence for decision-making than rational thinking expects, the jumping to conclusions (JTC) bias refers to the need for less evidence among individuals with schizophrenia/delusion compared to healthy people. Although the hippocampus-midbrain-striatal aberrant salience system and the salience, default mode (DMN), and frontoparietal networks ("triple networks") are implicated in delusion/schizophrenia pathophysiology, the associations between conservatism/JTC and these systems/networks are unclear.

METHODS

Thirty-seven patients with schizophrenia and 33 healthy controls performed the beads task, with large and small numbers of bead draws to decision (DTD) indicating conservatism and JTC, respectively. We performed independent component analysis (ICA) of resting functional magnetic resonance imaging (fMRI) data. For systems/networks above, we investigated interactions between diagnosis and DTD, and main effects of DTD. We similarly applied ICA to structural and diffusion MRI to explore the associations between DTD and gray/white matter.

RESULTS

We identified a significant main effect of DTD with functional connectivity between the striatum and DMN, which was negatively correlated with delusion severity in patients, indicating that the greater the anti-correlation between these networks, the stronger the JTC and delusion. We further observed the main effects of DTD on a gray matter network resembling the DMN, and a white matter network connecting the functional and gray matter networks (all P < 0.05, family-wise error [FWE] correction). Function and gray/white matter showed no significant interactions.

CONCLUSION

Our results support the novel association of conservatism and JTC biases with aberrant salience and default brain mode.

Alterations in Brain Morphometric Networks and Their Relationship with Memory Dysfunction in Patients with Type 2 Diabetes Mellitus

Cognitive dysfunction, a significant complication of type 2 diabetes mellitus (T2DM), can potentially manifest even from the early stages of the disease. Despite evidence of global brain atrophy and related cognitive dysfunction in early-stage T2DM patients, specific regions vulnerable to these changes have not yet been identified. The study enrolled patients with T2DM of less than five years' duration and without chronic complications (T2DM group, n=100) and demographically similar healthy controls (control group, n=50). High-resolution T1-weighted magnetic resonance imaging data were subjected to independent component analysis to identify structurally significant components indicative of morphometric networks. Within these networks, the groups' gray matter volumes were compared, and distinctions in memory performance were assessed. In the T2DM group, the relationship between changes in gray matter volume within these networks and declines in memory performance was examined. Among the identified morphometric networks, the T2DM group exhibited reduced gray matter volumes in both the precuneus (Bonferroni-corrected p=0.003) and insular-opercular (Bonferroni-corrected p=0.024) networks relative to the control group. Patients with T2DM demonstrated significantly lower memory performance than the control group (p=0.001). In the T2DM group, reductions in gray matter volume in both the precuneus (r=0.316, p=0.001) and insular-opercular (r=0.199, p=0.047) networks were correlated with diminished memory performance. Our findings indicate that structural alterations in the precuneus and insular-opercular networks, along with memory dysfunction, can manifest within the first 5 years following a diagnosis of T2DM.

Progressive increase of brain gray matter volume in individuals with regular soccer training

The study aimed to investigate alterations in gray matter volume in individuals undergoing regular soccer training, using high-resolution structural data, while also examining the temporal precedence of such structural alterations. Both voxel-based morphometry and source-based morphometry (SBM) methods were employed to analyze volumetric changes in gray matter between the soccer and control groups. Additionally, a causal network of structural covariance (CaSCN) was built using granger causality analysis on brain structural data ordering by training duration. Significant increases in gray matter volume were observed in the cerebellum in the soccer group. Additionally, the results of the SBM analysis revealed significant increases in gray matter volume in the calcarine and thalamus of the soccer group. The analysis of CaSCN demonstrated that the thalamus had a prominent influence on other brain regions in the soccer group, while the calcarine served as a transitional node, and the cerebellum acted as a prominent node that could be easily influenced by other brain regions. In conclusion, our study identified widely affected regions with increased gray matter volume in individuals with regular soccer training. Furthermore, a temporal precedence relationship among these regions was observed.

Mapping individual structural covariance network in development brain with dynamic time warping

A conspicuous property of brain development or maturity is coupled with coordinated or synchronized brain structural co-variation. However, there is still a lack of effective approach to map individual structural covariance network. Here, we developed a novel individual structural covariance network method using dynamic time warping algorithm and applied it to delineate developmental trajectories of topological organizations of structural covariance network from childhood to early adulthood with a large sample of 655 individuals from Human Connectome Project-Development dataset. We found that the individual structural covariance network exhibited small-worldness property and the network global topological characteristics including small-worldness, global efficiency, local efficiency, and modularity linearly increase with age while the shortest path length linearly decreases with age. The nodal topological properties including betweenness and degree increased with age in language and emotion regulation related brain areas, while it decreased with age mainly in visual cortex, sensorimotor area, and hippocampus. Moreover, the topological attributes of structural covariance network as features could predict the age of each individual. Taken together, our results demonstrate that dynamic time warping can effectively map individual structural covariance network to uncover the developmental trajectories of network topology, which may facilitate future investigations to establish the links of structural co-variations with respect to cognition and disease vulnerability.

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.

Altered structural covariance of locus coeruleus in individuals with significant memory concern and patients with mild cognitive impairment

The locus coeruleus (LC) is the site where tau accumulation is preferentially observed pathologically in Alzheimer's disease (AD) patients, but the changes in gray matter co-alteration patterns between the LC and the whole brain in the predementia phase of AD remain unclear. In this study, we estimated and compared the gray matter volume of the LC and its structural covariance (SC) with the whole brain among 161 normal healthy controls (HCs), 99 individuals with significant memory concern (SMC) and 131 patients with mild cognitive impairment (MCI). We found that SC decreased in MCI groups, which mainly involved the salience network and default mode network. These results imply that seeding from LC, the gray matter network disruption and disconnection appears early in the MCI group. The altered SC network seeding from the LC can serve as an imaging biomarker for discriminating the patients in the potential predementia phase of AD from the normal subjects.

Network Centrality and Modularity of Structural Covariance Networks in Posttraumatic Stress Disorder: A Multisite ENIGMA-PGC Study

Introduction: Cortical thickness (CT) and surface area (SA) are established biomarkers of brain pathology in posttraumatic stress disorder (PTSD). Structural covariance networks (SCNs) are represented as graphs with brain regions as nodes and correlations between nodes as edges. Methods: We built SCNs for PTSD and control groups using 148 CT and SA measures that were harmonized for site in n = 3439 subjects from Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA)-Psychiatric Genomics Consortium (PGC) PTSD. We compared centrality between PTSD and controls as well as interactions of diagnostic group with age, sex, and comorbid major depressive disorder (MDD) status. We investigated associations between network modularity and diagnostic grouping. Results: Nodes with higher CT-based centrality in PTSD compared with controls included the left inferior frontal sulcus, left fusiform gyrus, left superior temporal gyrus, and right inferior temporal gyrus. Children (<10 years) and adolescents (10-21) with PTSD showed greater centrality in frontotemporal areas compared with young (22-39) and middle-aged adults (40-59) with PTSD, who showed higher centrality in occipital areas. The PTSD diagnostic group interactions with sex and comorbid MDD showed altered centrality in occipital regions, along with greater visual network (VN) modularity in PTSD subjects compared with controls. Conclusion: Structural covariance in PTSD is associated with centrality differences in occipital areas and VN modularity differences in a large well-powered sample. In the context of extensive structural covariance remodeling taking place before and during adolescence, the present findings suggest a process of cortical remodeling that commences with trauma and/or the onset of PTSD but may also predate these events. Impact statement Centrality is a graph theory measure that offers insights into a node's relationship with all other nodes in the brain. Centrality pinpoints the drivers of brain communication within networks and nodes and may be a promising target for treatments such as neuromodulation. Modularity can pinpoint modules that exist within larger networks and quantify the connections between these modules. Centrality and modularity complement functional and structural connectivity measurements within specific brain networks.

Progressive structural and covariance connectivity abnormalities in patients with Alzheimer's disease

Background

Alzheimer's disease (AD) is one of most prevalent neurodegenerative diseases worldwide and characterized by cognitive decline and brain structure atrophy. While studies have reported substantial grey matter atrophy related to progression of AD, it remains unclear about brain regions with progressive grey matter atrophy, covariance connectivity, and the associations with cognitive decline in AD patients.

Objective

This study aims to investigate the grey matter atrophy, structural covariance connectivity abnormalities, and the correlations between grey matter atrophy and cognitive decline during AD progression.

Materials

We analyzed neuroimaging data of healthy controls (HC, n = 45) and AD patients (n = 40) at baseline (AD-T1) and one-year follow-up (AD-T2) obtained from the Alzheimer's Disease Neuroimaging Initiative. We investigated AD-related progressive changes of grey matter volume, covariance connectivity, and the clinical relevance to further understand the pathological progression of AD.

Results

The results showed clear patterns of grey matter atrophy in inferior frontal gyrus, prefrontal cortex, lateral temporal gyrus, posterior cingulate cortex, insula, hippocampus, caudate, and thalamus in AD patients. There was significant atrophy in bilateral superior temporal gyrus (STG) and left caudate in AD patients over a one-year period, and the grey matter volume decrease in right STG and left caudate was correlated with cognitive decline. Additionally, we found reduced structural covariance connectivity between right STG and left caudate in AD patients. Using AD-related grey matter atrophy as features, there was high discrimination accuracy of AD patients from HC, and AD patients at different time points.

Graph Theoretical Analysis of Structural Covariance Reveals the Relevance of Visuospatial and Attentional Areas in Essential Tremor Recovery After Stereotactic Radiosurgical Thalamotomy

Essential tremor (ET) is the most common movement disorder. Its pathophysiology is only partially understood. Here, we leveraged graph theoretical analysis on structural covariance patterns quantified from morphometric estimates for cortical thickness, surface area, and mean curvature in patients with ET before and one year after (to account for delayed clinical effect) ventro-intermediate nucleus (Vim) stereotactic radiosurgical thalamotomy. We further contrasted the observed patterns with those from matched healthy controls (HCs). Significant group differences at the level of individual morphometric properties were specific to mean curvature and the post-/pre-thalamotomy contrast, evidencing brain plasticity at the level of the targeted left thalamus, and of low-level visual, high-level visuospatial and attentional areas implicated in the dorsal visual stream. The introduction of cross-correlational analysis across pairs of morphometric properties strengthened the presence of dorsal visual stream readjustments following thalamotomy, as cortical thickness in the right lingual gyrus, bilateral rostral middle frontal gyrus, and left pre-central gyrus was interrelated with mean curvature in the rest of the brain. Overall, our results position mean curvature as the most relevant morphometric feature to understand brain plasticity in drug-resistant ET patients following Vim thalamotomy. They also highlight the importance of examining not only individual features, but also their interactions, to gain insight into the routes of recovery following intervention.

A transdiagnostic review of neuroimaging studies of apathy and disinhibition in dementia

Apathy and disinhibition are common and highly distressing neuropsychiatric symptoms associated with negative outcomes in persons with dementia. This paper is a critical review of functional and structural neuroimaging studies of these symptoms transdiagnostically in dementia of the Alzheimer type, which is characterized by prominent amnesia early in the disease course, and behavioural variant frontotemporal dementia, characterized by early social-comportmental deficits. We describe the prevalence and clinical correlates of these symptoms and describe methodological issues, including difficulties with symptom definition and different measurement instruments. We highlight the heterogeneity of findings, noting however, a striking similarity of the set of brain regions implicated across clinical diagnoses and symptoms. These regions involve several key nodes of the salience network, and we describe the functions and anatomical connectivity of these brain areas, as well as present a new theoretical account of disinhibition in dementia. Future avenues for research are discussed, including the importance of transdiagnostic studies, measuring subdomains of apathy and disinhibition, and examining different units of analysis for deepening our understanding of the networks and mechanisms underlying these extremely distressing symptoms.

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.

Decreased gray matter volume is associated with theory of mind deficit in adolescents with schizophrenia

Schizophrenia patients often suffer from deficit in theory of mind (TOM). Prior neuroimaging studies revealed neuroimaging correlates of TOM deficit in adults with schizophrenia, neuroimaging correlates of TOM in adolescents is less well established. This study aimed to investigate gray matter volume (GMV) abnormalities and TOM deficits in schizophrenic adolescents, and examine the relationship between them. Twenty adolescent schizophrenic patients and 25 age, sex-matched healthy controls underwent T1-weighted magnetic resonance imaging (MRI) scans, and were examined for TOM based on the Reading the Mind in the Eyes test (RMET). Univariate voxel-based morphometry (VBM) and multivariate source-based morphometry (SBM) were employed to examine alterations of two GMV phenotypes in schizophrenic adolescents: voxel-wise GMV and covarying structural brain patterns (SBPs). Compared with controls, our results revealed a significant deficit in RMET performance of the patients, Voxel-wise VBM analysis revealed that patients exhibited decreased GMV in bilateral insula, orbitofrontal cortex, and right rolandic operculum, and GMV of these brain regions were positively correlated with RMET performance. Multivariate SBM analysis identified a significantly different between-group SBP comprising of bilateral insula and inferior frontal cortex, bilateral superior temporal cortex, and bilateral lateral parietal cortex and right rolandic operculum. The loading scores of this SBP was positively correlated with RMET performance. This study revealed impairment of TOM ability in schizophrenic adolescents and revealed an association between TOM deficit and decreased GMV in regions which are crucial for social cognition, thereby provided insight and possible target regions for understanding the neural pathology and normalizing TOM deficit in adolescent schizophrenia patients.

Structural covariance pattern abnormalities of insula in major depressive disorder: A CAN-BIND study report

BACKGROUND AND METHODS

Investigation of the insula may inform understanding of the etiopathogenesis of major depressive disorder (MDD). In the present study, we introduced a novel gray matter volume (GMV) based structural covariance technique, and applied it to a multi-centre study of insular subregions of 157 patients with MDD and 93 healthy controls from the Canadian Biomarker Integration Network in Depression (CAN-BIND, https://www.canbind.ca/). Specifically, we divided the unilateral insula into three subregions, and investigated their coupling with whole-brain GMV-based structural brain networks (SBNs). We compared between-group difference of the structural coupling patterns between the insular subregions and SBNs.

RESULTS

The insula was divided into three subregions, including an anterior one, a superior-posterior one and an inferior-posterior one. In the comparison between MDD patients and controls we found that patients' right anterior insula showed increased inter-network coupling with the default mode network, and it showed decreased inter-network coupling with the central executive network; whereas patients' right ventral-posterior insula showed decreased inter-network coupling with the default mode network, and it showed increased inter-network coupling with the central executive network. We also demonstrated that patients' loading parameters of the right ventral-posterior insular structural covariance negatively correlated with their suicidal ideation scores; and controls' loading parameters of the right ventral-posterior insular structural covariance positively correlated with their motor and psychomotor speed scores, whereas these phenomena were not found in patients. Additionally, we did not find significant inter-network coupling between the whole-brain SBNs, including salience network, default mode network, and central executive network.

CONCLUSIONS

Our work proposed a novel technique to investigate the structural covariance coupling between large-scale structural covariance networks, and provided further evidence that MDD is a system-level disorder that shows disrupted structural coupling between brain networks.

Dementia with Lewy bodies: association of Alzheimer pathology with functional connectivity networks

Dementia with Lewy bodies (DLB) is neuropathologically defined by the presence of α-synuclein aggregates, but many DLB cases show concurrent Alzheimer's disease pathology in the form of amyloid-β plaques and tau neurofibrillary tangles. The first objective of this study was to investigate the effect of Alzheimer's disease co-pathology on functional network changes within the default mode network (DMN) in DLB. Second, we studied how the distribution of tau pathology measured with PET relates to functional connectivity in DLB. Twenty-seven DLB, 26 Alzheimer's disease and 99 cognitively unimpaired participants (balanced on age and sex to the DLB group) underwent tau-PET with AV-1451 (flortaucipir), amyloid-β-PET with Pittsburgh compound-B (PiB) and resting-state functional MRI scans. The resing-state functional MRI data were used to assess functional connectivity within the posterior DMN. This was then correlated with overall cortical flortaucipir PET and PiB PET standardized uptake value ratio (SUVr). The strength of interregional functional connectivity was assessed using the Schaefer atlas. Tau-PET covariance was measured as the correlation in flortaucipir SUVr between any two regions across participants. The association between region-to-region functional connectivity and tau-PET covariance was assessed using linear regression. Additionally, we identified the region with highest and the region with lowest tau SUVrs (tau hot- and cold spots) and tested whether tau SUVr in all other brain regions was associated with the strength of functional connectivity to these tau hot and cold spots. A reduction in posterior DMN connectivity correlated with overall higher cortical tau- (r = -0.39, P = 0.04) and amyloid-PET uptake (r = -0.41, P = 0.03) in the DLB group, i.e. patients with DLB who have more concurrent Alzheimer's disease pathology showed a more severe loss of DMN connectivity. Higher functional connectivity between regions was associated with higher tau covariance in cognitively unimpaired, Alzheimer's disease and DLB. Furthermore, higher functional connectivity of a target region to the tau hotspot (i.e. inferior/medial temporal cortex) was related to higher flortaucipir SUVrs in the target region, whereas higher functional connectivity to the tau cold spot (i.e. sensory-motor cortex) was related to lower flortaucipir SUVr in the target region. Our findings suggest that a higher burden of Alzheimer's disease co-pathology in patients with DLB is associated with more Alzheimer's disease-like changes in functional connectivity. Furthermore, we found an association between the brain's functional network architecture and the distribution of tau pathology that has recently been described in Alzheimer's disease. We show that this relationship also exists in patients with DLB, indicating that similar mechanisms of connectivity-dependent occurrence of tau pathology might be at work in both diseases.

Identifying Subgroups of Major Depressive Disorder Using Brain Structural Covariance Networks and Mapping of Associated Clinical and Cognitive Variables

Background

Identifying data-driven subtypes of major depressive disorder (MDD) holds promise for parsing the heterogeneity of MDD in a neurobiologically informed way. However, limited studies have used brain structural covariance networks (SCNs) for subtyping MDD.

Methods

This study included 145 unmedicated patients with MDD and 206 demographically matched healthy control subjects, who underwent a structural magnetic resonance imaging scan and a comprehensive neurocognitive battery. Patterns of structural covariance were identified using source-based morphometry across both patients with MDD and healthy control subjects. K-means clustering algorithms were applied on dysregulated structural networks in MDD to identify potential MDD subtypes. Finally, clinical and neurocognitive measures were compared between identified subgroups to elucidate the profile of these MDD subtypes.

Results

Source-based morphometry across all individuals identified 28 whole-brain SCNs that encompassed the prefrontal, anterior cingulate, and orbitofrontal cortices; basal ganglia; and cerebellar, visual, and motor regions. Compared with healthy control subjects, individuals with MDD showed lower structural network integrity in three networks including default mode, ventromedial prefrontal cortical, and salience networks. Clustering analysis revealed two MDD subtypes based on the patterns of structural network abnormalities in these three networks. Further profiling revealed that patients in subtype 1 had younger age of onset and more symptom severity as well as greater deficits in cognitive performance than patients in subtype 2.

Conclusions

Overall, we identified two MDD subtypes based on SCNs that differed in their clinical and cognitive profile. Our results represent a proof-of-concept framework for leveraging these large-scale SCNs to parse heterogeneity in MDD.

Disrupted anterior and posterior hippocampal structural networks correlate impaired verbal memory and spatial memory in different subtypes of mild cognitive impairment

BACKGROUND AND PURPOSE

The anterior and posterior hippocampal networks represent verbal and spatial memory, respectively, and may play different roles in the pathological mechanism of amnestic mild cognitive impairment (aMCI) and non-amnestic MCI (naMCI), which has not been explored.

METHODS

A total of 990 older adults with 791 normal controls (NCs) (65 ± 6 years, 502 women), 140 aMCI (66 ± 7 years, 84 women) and 59 naMCI (66 ± 7 years, 38 women) were included. A multivariate method, partial least squares, was used to assess the structural covariance networks of the anterior hippocampus (aHC) and posterior hippocampus (pHC), and their relationships with verbal memory and spatial memory in the three groups.

RESULTS

Three aHC and pHC structural covariance network patterns emerged: (1) the age pattern; (2) the specific aMCI pattern; and (3) the spatial memory pattern. Furthermore, aMCI patients had more extensive and severe damage in the three patterns, and correlated with greater decline in verbal memory, which was mainly characterized by the aHC network.

CONCLUSIONS

The aMCI and naMCI showed different patterns and damage in the structural covariance networks, and functional segregation of the aHC and pHC networks still exists in the process of pathological aging. A potential neural explanation is provided for the conversion of aMCI and naMCI into different types of dementia in the future.

Homogeneous grey matter patterns in patients with obsessive-compulsive disorder

BACKGROUND

Changes in grey matter volume have frequently been reported in patients with obsessive-compulsive disorder (OCD). Most studies performed whole brain or region-of-interest based analyses whereas grey matter volume based on structural covariance networks has barely been investigated up to now. Therefore, the present study investigated grey matter volume within structural covariance networks in a sample of 228 participants (n = 117 OCD patients, n = 111 healthy controls).

METHODS

First, an independent component analysis (ICA) was performed on all subjects' preprocessed T1 images to derive covariance-dependent morphometric networks. Then, grey matter volume from each of the ICA-derived morphometric networks was extracted and compared between the groups. In addition, we performed logistic regressions and receiver operating characteristic (ROC) analyses to investigate whether network-related grey matter volume could serve as a characteristic that allows to differentiate patients from healthy volunteers. Moreover, we assessed grey matter pattern organization by correlating grey matter volume in all networks across all participants. Finally, we explored a potential association between grey matter volume or whole-brain grey matter pattern organization and clinical characteristics in terms of symptom severity and duration of illness.

RESULTS

There were only subtle group differences in network-related grey matter volume. Network-related grey matter volume had moreover a very poor discrimination performance. We found, however, significant group differences with regard to grey matter pattern organization. When correlating grey matter volume in all networks across all participants, patients showed a significantly higher homogeneity across all networks and a significantly lower heterogeneity, as assessed by the coefficient of variation across all networks as well as in several single networks. There was no association with clinical characteristics.

CONCLUSION

The findings of the present study suggest that the pathological mechanisms of OCD reduce interindividual grey matter variability. We assume that common characteristics associated with the disorder may lead to a more uniform, disorder-specific morphometry.

Altered structural covariance of hippocampal subregions in patients with Alzheimer's disease

BACKGROUND AND PURPOSE

Different atrophy of hippocampus subregions is a valuable indicator of patients with Alzheimer's disease (AD). To explore the relationship among the hippocampal subregions of patients with AD, altered gray matter structural covariance of hippocampal subregions in patients with AD was studied.

MATERIALS AND METHODS

Participants were selected from the Open Access Series of Imaging Studies Database. Pearson correlations among the volume of the hippocampal subregions were generated as structural covariance network. Topological metrics for all selected sparsity ranges were calculated in the healthy controls (HCs) and patients with AD by using the GRETNA software package. Spearman correlation analysis was performed to statistically analyze the volume and Mini-mental State Examination (MMSE) scores of the hippocampal subregions of the patients with AD, with age and gender as interference covariates and corrected for false discovery rate (FDR) (p < 0.05).

RESULTS

The structural covariance network properties of the hippocampal subregions of patients with AD changed. The clustering coefficient (Cp) and network efficiency (Ne) decreased, characteristic path length (Lp) increased, and the hub nodes changed. The volumes of left parasubiculum, right granule cell layer of dentate gyrus (GC-DG), right molecular layer of the hippocampus (molecular_layer_HP), right Cornu Ammonis (CA) regions CA1 of the hippocampus proper, right fimbria and right CA4 were significantly correlated with the MMSE scores.

CONCLUSIONS

The structural covariance network of the hippocampal subregions of patients with AD was reorganized, and the transmission efficiency was weakened. This study explored the changes in these subregions from the network level, which may provide a new perspective and theoretical basis for the neurobiological mechanisms of patients with AD.

Sex effects on cortical morphological networks in healthy young adults

Understanding sex-related differences across the human cerebral cortex is an important step in elucidating the basis of psychological, behavioural and clinical differences between the sexes. Prior structural neuroimaging studies primarily focused on regional sex differences using univariate analyses. Here we focus on sex differences in cortical morphological networks (CMNs) derived using multivariate modelling of regional cortical measures of volume and surface from high-quality structural MRI scans from healthy participants in the Human Connectome Project (HCP) (n = 1,063) and the Southwest University Longitudinal Imaging Multimodal (SLIM) study (n = 549). The functional relevance of the CMNs was inferred using the NeuroSynth decoding function. Sex differences were widespread but not uniform. In general, females had higher volume, thickness and cortical folding in networks that involve prefrontal (both ventral and dorsal regions including the anterior cingulate) and parietal regions while males had higher volume, thickness and cortical folding in networks that primarily include temporal and posterior cortical regions. CMN loading coefficients were used as input features to linear discriminant analyses that were performed separately in the HCP and SLIM; sex was predicted with a high degree of accuracy (81%-85%) across datasets. The availability of behavioral data in the HCP enabled us to show that male-biased surface-based CMNs were associated with externalizing behaviors. These results extend previous literature on regional sex-differences by identifying CMNs that can reliably predict sex, are relevant to the expression of psychopathology and provide the foundation for the future investigation of their functional significance in clinical populations.

Improving Individual Brain Age Prediction Using an Ensemble Deep Learning Framework

Brain age is an imaging-based biomarker with excellent feasibility for characterizing individual brain health and may serve as a single quantitative index for clinical and domain-specific usage. Brain age has been successfully estimated using extensive neuroimaging data from healthy participants with various feature extraction and conventional machine learning (ML) approaches. Recently, several end-to-end deep learning (DL) analytical frameworks have been proposed as alternative approaches to predict individual brain age with higher accuracy. However, the optimal approach to select and assemble appropriate input feature sets for DL analytical frameworks remains to be determined. In the Predictive Analytics Competition 2019, we proposed a hierarchical analytical framework which first used ML algorithms to investigate the potential contribution of different input features for predicting individual brain age. The obtained information then served as a priori knowledge for determining the input feature sets of the final ensemble DL prediction model. Systematic evaluation revealed that ML approaches with multiple concurrent input features, including tissue volume and density, achieved higher prediction accuracy when compared with approaches with a single input feature set [Ridge regression: mean absolute error (MAE) = 4.51 years, R ² = 0.88; support vector regression, MAE = 4.42 years, R ² = 0.88]. Based on this evaluation, a final ensemble DL brain age prediction model integrating multiple feature sets was constructed with reasonable computation capacity and achieved higher prediction accuracy when compared with ML approaches in the training dataset (MAE = 3.77 years; R ² = 0.90). Furthermore, the proposed ensemble DL brain age prediction model also demonstrated sufficient generalizability in the testing dataset (MAE = 3.33 years). In summary, this study provides initial evidence of how-to efficiency for integrating ML and advanced DL approaches into a unified analytical framework for predicting individual brain age with higher accuracy. With the increase in large open multiple-modality neuroimaging datasets, ensemble DL strategies with appropriate input feature sets serve as a candidate approach for predicting individual brain age in the future.

Gray matter covariations and core symptoms of autism: the EU-AIMS Longitudinal European Autism Project

BACKGROUND

Voxel-based morphometry (VBM) studies in autism spectrum disorder (autism) have yielded diverging results. This might partly be attributed to structural alterations being associating with the combined influence of several regions rather than with a single region. Further, these structural covariation differences may relate to continuous measures of autism rather than with categorical case-control contrasts. The current study aimed to identify structural covariation alterations in autism, and assessed canonical correlations between brain covariation patterns and core autism symptoms.

METHODS

We studied 347 individuals with autism and 252 typically developing individuals, aged between 6 and 30 years, who have been deeply phenotyped in the Longitudinal European Autism Project. All participants' VBM maps were decomposed into spatially independent components using independent component analysis. A generalized linear model (GLM) was used to examine case-control differences. Next, canonical correlation analysis (CCA) was performed to separately explore the integrated effects between all the brain sources of gray matter variation and two sets of core autism symptoms.

RESULTS

GLM analyses showed significant case-control differences for two independent components. The first component was primarily associated with decreased density of bilateral insula, inferior frontal gyrus, orbitofrontal cortex, and increased density of caudate nucleus in the autism group relative to typically developing individuals. The second component was related to decreased densities of the bilateral amygdala, hippocampus, and parahippocampal gyrus in the autism group relative to typically developing individuals. The CCA results showed significant correlations between components that involved variation of thalamus, putamen, precentral gyrus, frontal, parietal, and occipital lobes, and the cerebellum, and repetitive, rigid and stereotyped behaviors and abnormal sensory behaviors in autism individuals.

LIMITATIONS

Only 55.9% of the participants with autism had complete questionnaire data on continuous parent-reported symptom measures.

CONCLUSIONS

Covaried areas associated with autism diagnosis and/or symptoms are scattered across the whole brain and include the limbic system, basal ganglia, thalamus, cerebellum, precentral gyrus, and parts of the frontal, parietal, and occipital lobes. Some of these areas potentially subserve social-communicative behavior, whereas others may underpin sensory processing and integration, and motor behavior.

Reliability and comparability of human brain structural covariance networks

Structural covariance analysis is a widely used structural MRI analysis method which characterises the co-relations of morphology between brain regions over a group of subjects. To our knowledge, little has been investigated in terms of the comparability of results between different data sets of healthy human subjects, as well as the reliability of results over the same subjects in different rescan sessions, image resolutions, or FreeSurfer versions. In terms of comparability, our results show substantial differences in the structural covariance matrix between data sets of age- and sex-matched healthy human adults. These differences persist after univariate site correction, they are exacerbated by low sample sizes, and they are most pronounced when using average cortical thickness as a morphological measure. Down-stream graph theoretic analyses further show statistically significant differences. In terms of reliability, substantial differences were also found when comparing repeated scan sessions of the same subjects, image resolutions, and even FreeSurfer versions of the same image. We could further estimate the relative measurement error and showed that it is largest when using cortical thickness as a morphological measure. Using simulated data, we argue that cortical thickness is least reliable because of larger relative measurement errors. Practically, we make the following recommendations (1) combining subjects across sites into one group should be avoided, particularly if sites differ in image resolutions, subject demographics, or preprocessing steps; (2) surface area and volume should be preferred as morphological measures over cortical thickness; (3) a large number of subjects (n≫30 for the Desikan-Killiany parcellation) should be used to estimate structural covariance; (4) measurement error should be assessed where repeated measurements are available; (5) if combining sites is critical, univariate (per ROI) site-correction is insufficient, but error covariance (between ROIs) should be explicitly measured and modelled.

Disrupted structural covariance network in first episode schizophrenia patients: Evidence from a large sample MRI-based morphometric study

BACKGROUND

Recent progress in neuroscience research has provided evidence that schizophrenia is a disease that involves dysconnectivity of brain networks. Widespread gray matter loss was commonly observed but how these gray matter abnormalities are characterized at the large-scale network-level in schizophrenia, especially patients with first-episode (FE-SCZ) remains unclear.

METHODS

In this study, gray matter structural network aberrations were investigated by applying structural covariance network analysis to 193 first episode schizophrenia patients and 178 age and gender-matched healthy controls (HCs). The mean gray matter volume in seed regions relating to eight specific networks (visual, auditory, sensorimotor, speech, semantic, default-mode, executive control, and salience) were extracted, and voxel-wise analyses of covariance were conducted to compare the association between whole-brain gray matter volume and each seed region for FE-SCZ and HCs.

RESULTS

The auditory network was less extended in FE-SCZ compared with HCs, with a significant decrease in the structural association between the Hesch's gyrus and the middle frontal gyrus and the superior frontal gyrus. Hyperconnectivity was observed in executive control network with a significant increase in the structural association between the dorsal lateral prefrontal cortex and the superior frontal gyrus and supplementary motor area.

CONCLUSION

Our research shows that seed based structural covariance analysis can well characterize multiple large-scale networks, the observed changes might underly the hallucinations and cognitive impairments observed in FE-SCZ. Given that these patients were experiencing their first episode of schizophrenia, our findings suggest that such structural network deficits are present at an early stage in this disorder.

Structural Covariance Network Disruption and Functional Compensation in Parkinson's Disease

Purpose: To investigate the structural covariance network disruption in Parkinson’s disease (PD), and explore the functional alterations of disrupted structural covariance network.

Methods: A cohort of 100 PD patients and 70 healthy participants underwent structural and functional magnetic resonance scanning. Independent component analysis (ICA) was applied separately to both deformation-based morphometry (DBM) maps and functional maps with the same calculating parameters (both decomposed into 20 independent components (ICs) and computed 20 times the Infomax algorithm in ICASSO). Disrupted structural covariance network in PD patients was identified, and then, we performed goodness of fit analysis to obtain the functional network that showed the highest spatial overlap with it. We investigated the relationship between structural covariance network and functional network alterations. Finally, to further understand the structural and functional alterations over time, we performed a longitudinal subgroup analysis (51 patients were followed up for 2 years) with the same procedures.

Results: In a cross-sectional analysis, PD patients showed decreased structural covariance between anterior and posterior cingulate subnetworks. The functional components showed best overlap with anterior and posterior cingulate structural subnetworks were selected as anterior and posterior cingulate functional subnetworks. The functional connectivity between them was significantly increased [assessed by Functional Network Connectivity (FNC) toolbox]; and the increased functional connectivity was negatively correlated with cingulate structural covariance network integrity. Longitudinal subgroup analysis showed cingulate structural covariance network disruption was worse at follow-up, while the functional connectivity between anterior and posterior cingulate network was increased at baseline and decreased at follow-up.

Conclusion: This study indicated that the cingulate structural covariance network displayed a high susceptibility in PD patients. This study indicated that the cingulate structural covariance network displayed a high susceptibility in PD patients. Considering that disrupted structural covariance network coexisted with enhanced/remained functional activity during disease development, enhanced functional activity underlying the disrupted cingulate structural covariance network might represent a temporal compensation for maintaining clinical performance.

Large-Scale Structural Covariance Networks Predict Age in Middle-to-Late Adulthood: A Novel Brain Aging Biomarker

The aging process is accompanied by changes in the brain's cortex at many levels. There is growing interest in summarizing these complex brain-aging profiles into a single, quantitative index that could serve as a biomarker both for characterizing individual brain health and for identifying neurodegenerative and neuropsychiatric diseases. Using a large-scale structural covariance network (SCN)-based framework with machine learning algorithms, we demonstrate this framework's ability to predict individual brain age in a large sample of middle-to-late age adults, and highlight its clinical specificity for several disease populations from a network perspective. A proposed estimator with 40 SCNs could predict individual brain age, balancing between model complexity and prediction accuracy. Notably, we found that the most significant SCN for predicting brain age included the caudate nucleus, putamen, hippocampus, amygdala, and cerebellar regions. Furthermore, our data indicate a larger brain age disparity in patients with schizophrenia and Alzheimer's disease than in healthy controls, while this metric did not differ significantly in patients with major depressive disorder. These findings provide empirical evidence supporting the estimation of brain age from a brain network perspective, and demonstrate the clinical feasibility of evaluating neurological diseases hypothesized to be associated with accelerated brain aging.

Functional Connectivity in Neurodegenerative Disorders: Alzheimer's Disease and Frontotemporal Dementia

Neurodegenerative disorders are a growing cause of morbidity and mortality worldwide. Onset is typically insidious and clinical symptoms of behavioral change, memory loss, or cognitive dysfunction may not be evident early in the disease process. Efforts have been made to discover biomarkers that allow for earlier diagnosis of neurodegenerative disorders, to initiate treatment that may slow the course of clinical deterioration. Neuronal dysfunction occurs earlier than clinical symptoms manifest. Thus, assessment of neuronal function using functional brain imaging has been examined as a potential biomarker. While most early studies used task-functional magnetic resonance imaging (fMRI), with the more recent technique of resting-state fMRI, "intrinsic" relationships between brain regions or brain networks have been studied in greater detail in neurodegenerative disorders. In Alzheimer's disease, the most common neurodegenerative disorder, and frontotemporal dementia, another of the common dementias, specific brain networks may be particularly susceptible to dysfunction. In this review, we highlight the major findings of functional connectivity assessed by resting state fMRI in Alzheimer's disease and frontotemporal dementia.

Gray Matter Covariance Networks as Classifiers and Predictors of Cognitive Function in Alzheimer's Disease

The study of shared variation in gray matter morphology may define neurodegenerative diseases beyond what can be detected from the isolated assessment of regional brain volumes. We, therefore, aimed to (1) identify SCNs (structural covariance networks) that discriminate between Alzheimer's disease (AD) patients and healthy controls (HC), (2) investigate their diagnostic accuracy in comparison and above established markers, and (3) determine if they are associated with cognitive abilities. We applied a random forest algorithm to identify discriminating networks from a set of 20 SCNs. The algorithm was trained on a main sample of 104 AD patients and 104 age-matched HC and was then validated in an independent sample of 28 AD patients and 28 controls from another center. Only two of the 20 SCNs contributed significantly to the discrimination between AD and controls. These were a temporal and a secondary somatosensory SCN. Their diagnostic accuracy was 74% in the original cohort and 80% in the independent samples. The diagnostic accuracy of SCNs was comparable with that of conventional volumetric MRI markers including whole brain volume and hippocampal volume. SCN did not significantly increase diagnostic accuracy beyond that of conventional MRI markers. We found the temporal SCN to be associated with verbal memory at baseline. No other associations with cognitive functions were seen. SCNs failed to predict the course of cognitive decline over an average of 18 months. We conclude that SCNs have diagnostic potential, but the diagnostic information gain beyond conventional MRI markers is limited.

Extended multimodal whole-brain anatomical covariance analysis: detection of disrupted correlation networks related to amyloid deposition

Background

An anatomical covariance analysis (ACA) enables to elucidate inter-regional connections on a group basis, but little is known about the connections among white matter structures or among gray and white matter structures. Effect of including multiple magnetic resonance imaging (MRI) modalities into ACA framework in detecting white-to-white or gray-to-white connections is yet to be investigated.

New method

Proposed extended anatomical covariance analysis (eACA), analyzes correlations among gray and white matter structures (multi-structural) in various types of imaging modalities (T1-weighted images, T2 maps obtained from dual-echo sequences, and diffusion tensor images (DTI)). To demonstrate the capability to detect a disruption of the correlation network affected by pathology, we applied the eACA to two groups of cognitively-normal elderly individuals, one with (PiB+) and one without (PiB-) amyloid deposition in their brains.

Results

The volume of each anatomical structure was symmetric and functionally related structures formed a cluster. The pseudo-T2 value was highly homogeneous across the entire cortex in the PiB- group, while a number of physiological correlations were altered in the PiB + group. The DTI demonstrated unique correlation network among structures within the same phylogenetic portions of the brain that were altered in the PiB + group.

Comparison with Existing Method

The proposed eACA expands the concept of existing ACA to the connections among the white matter structures. The extension to other image modalities expands the way in which connectivity may be detected.

Conclusion

The eACA has potential to evaluate alterations of the anatomical network related to pathological processes.

Structural network integrity of the central executive network is associated with the therapeutic effect of rTMS in treatment resistant depression

Repetitive transcranial magnetic stimulation (rTMS) is a first-line option for treatment-resistant depression (TRD), but prediction of treatment outcome remains a clinical challenge. The present study aimed to compare structural and functional covariance networks (SCNs and FCNs) between remitters and nonremitters. We determined the predictive capacities of SCNs and FCNs to discriminate the two groups. Fifty TRD patients underwent a course of rTMS to the left dorsolateral prefrontal cortex. They were categorized into remitters (n = 22) and nonremitters (n = 28) based on HDRS≤7 at the end of treatment. Baseline structural and functional magnetic imaging (sMRI and fMRI) of the patients and 42 healthy controls were collected. SCNs and FCNs were defined based on structural and functional covariance of gray mater volume (GMV) and fractional amplitude of low-frequency fluctuations (fALFF) from sMRI and fMRI, respectively. Structural/functional network integrity of these networks (default mode network [DMN], central executive network [CEN] and salience network [SN]) were compared between the three groups. In patients, associations between SCNs and FCNs with clinical improvements were studied using linear correlation analysis. Receiver-operating characteristic (ROC) analysis was conducted to confirm the utility of the SCNs and FCNs in classifying clinical sub-groups. Nonremitters exhibited lower structural integrity in CEN than remitters and controls. Higher structural integrity of CEN was related to clinical improvement (r = 0.423, p = .002), and structural integrity distinguished remitters and nonremitters with a fairly high accuracy (AUC = 0.71, p = .008). No group differences or correlation with clinical changes were found in FCNs. Results suggest the CEN may play a role mediating clinical improvement in rTMS for depression. Structural covariance networks may be features to consider in prediction of clinical improvement.

Exploring quantitative group-wise differentiation of Alzheimer's disease and behavioural variant frontotemporal dementia using tract-specific microstructural white matter and functional connectivity measures at multiple time points

Objectives

This study explored group-wise quantitative measures of tract-specific white matter (WM) microstructure and functional default mode network (DMN) connectivity to establish an initial indication of their clinical applicability for early-stage and follow-up differential diagnosis of Alzheimer’s disease (AD) and behavioural variant frontotemporal dementia (bvFTD).

Methods

Eleven AD and 12 bvFTD early-stage patients and 18 controls underwent diffusion tensor imaging and resting state functional magnetic resonance imaging at 3 T. All AD and 6 bvFTD patients underwent the same protocol at 1-year follow-up. Functional connectivity measures of DMN and WM tract-specific diffusivity measures were determined for all groups. Exploratory analyses were performed to compare all measures between the three groups at baseline and between patients at follow-up. Additionally, the difference between baseline and follow-up diffusivity measures in AD and bvFTD patients was compared.

Results

Functional connectivity of the DMN was not different between groups at baseline and at follow-up. Diffusion abnormalities were observed widely in bvFTD and regionally in the hippocampal cingulum in AD. The extent of the differences between bvFTD and AD was diminished at follow-up, yet abnormalities were still more pronounced in bvFTD. The rate of change was similar in bvFTD and AD.

Conclusions

This study provides a tentative indication that quantitative tract-specific microstructural WM abnormalities, but not quantitative functional connectivity of the DMN, may aid early-stage and follow-up differential diagnosis of bvFTD and AD. Specifically, pronounced microstructural changes in anterior WM tracts may characterise bvFTD, whereas microstructural abnormalities of the hippocampal cingulum may characterise AD.

Key Points

• The clinical applicability of quantitative brain imaging measures for early-stage and follow-up differential diagnosis of dementia subtypes was explored using a group-wise approach.

• Quantitative tract-specific microstructural white matter abnormalities, but not quantitative functional connectivity of the default mode network, may aid early-stage and follow-up differential diagnosis of behavioural variant frontotemporal dementia and Alzheimer’s disease.

• Pronounced microstructural white matter (WM) changes in anterior WM tracts characterise behavioural variant frontotemporal dementia, whereas microstructural WM abnormalities of the hippocampal cingulum in the absence of other WM changes characterise Alzheimer’s disease.

Electronic supplementary material

The online version of this article (10.1007/s00330-019-06061-7) contains supplementary material, which is available to authorized users.

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.

Cingulate-centered large-scale networks: Normal functions, aging, and neurodegenerative disease

In this chapter, we review evidence from structural and functional neuroimaging in humans to consider the role of the cingulate cortex subregions (i.e., subgenual anterior cingulate cortex, pregenual anterior cingulate cortex, anterior midcingulate cortex, and dorsal posterior cingulate cortex) as major hubs anchoring multiple large-scale brain networks. We begin with a review of evidence from intrinsic functional connectivity and diffusion tensor imaging studies to show how connections within and between cingulate-centered networks contribute to processing and integrating signals related to autonomic, affective, executive, and memory functions. We then consider how variability in cingulate-centered networks could contribute to a range of aging outcomes, including typical aging and unusually successful aging (dubbed "superaging"), as well as early neurodegenerative dementias, including frontotemporal dementia and Alzheimer's disease.

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.

Grey-matter network disintegration as predictor of cognitive and motor function with aging

Loss of grey-matter volume with advancing age affects the entire cortex. It has been suggested that atrophy occurs in a network-dependent manner with advancing age rather than in independent brain areas. The relationship between networks of structural covariance (SCN) disintegration and cognitive functioning during normal aging is not fully explored. We, therefore, aimed to (1) identify networks that lose GM integrity with advancing age, (2) investigate if age-related impairment of integrity in GM networks associates with cognitive function and decreasing fine motor skills (FMS), and (3) examine if GM disintegration is a mediator between age and cognition and FMS. T1-weighted scans of n = 257 participants (age range: 20–87) were used to identify GM networks using independent component analysis. Random forest analysis was implemented to examine the importance of network integrity as predictors of memory, executive functions, and FMS. The associations between GM disintegration, age and cognitive performance, and FMS were assessed using mediation analyses. Advancing age was associated with decreasing cognitive performance and FMS. Fourteen of 20 GM networks showed integrity changes with advancing age. Next to age and education, eight networks (fronto-parietal, fronto-occipital, temporal, limbic, secondary somatosensory, cuneal, sensorimotor network, and a cerebellar network) showed an association with cognition and FMS (up to 15.08%). GM networks partially mediated the effect between age and cognition and age and FMS. We confirm an age-related decline in cognitive functioning and FMS in non-demented community-dwelling subjects and showed that aging selectively affects the integrity of GM networks. The negative effect of age on cognition and FMS is associated with distinct GM networks and is partly mediated by their disintegration.

Structural covariance networks across the life span, from 6 to 94 years of age

Structural covariance examines covariation of gray matter morphology between brain regions and across individuals. Despite significant interest in the influence of age on structural covariance patterns, no study to date has provided a complete life span perspective—bridging childhood with early, middle, and late adulthood—on the development of structural covariance networks. Here, we investigate the life span trajectories of structural covariance in six canonical neurocognitive networks: default, dorsal attention, frontoparietal control, somatomotor, ventral attention, and visual. By combining data from five open-access data sources, we examine the structural covariance trajectories of these networks from 6 to 94 years of age in a sample of 1,580 participants. Using partial least squares, we show that structural covariance patterns across the life span exhibit two significant, age-dependent trends. The first trend is a stable pattern whose integrity declines over the life span. The second trend is an inverted-U that differentiates young adulthood from other age groups. Hub regions, including posterior cingulate cortex and anterior insula, appear particularly influential in the expression of this second age-dependent trend. Overall, our results suggest that structural covariance provides a reliable definition of neurocognitive networks across the life span and reveal both shared and network-specific trajectories.

The importance of life span perspectives is increasingly apparent in understanding normative interactions of large-scale neurocognitive networks. Although recent work has made significant strides in understanding the functional and structural connectivity of these networks, there has been comparatively little attention to life span trajectories of structural covariance networks. In this study we examine patterns of structural covariance across the life span for six neurocognitive networks. Our results suggest that networks exhibit both network-specific stable patterns of structural covariance as well as shared age-dependent trends. Previously identified hub regions seem to show a strong influence on the expression of these age-related trajectories. These results provide initial evidence for a multimodal understanding of structural covariance in network structure-function interaction across the life course.

Loss of integrity and atrophy in cingulate structural covariance networks in Parkinson's disease

BACKGROUND

In Parkinson's disease (PD), the relation between cortical brain atrophy on MRI and clinical progression is not straightforward. Determination of changes in structural covariance networks - patterns of covariance in grey matter density - has shown to be a valuable technique to detect subtle grey matter variations. We evaluated how structural network integrity in PD is related to clinical data.

METHODS

3 Tesla MRI was performed in 159 PD patients. We used nine standardized structural covariance networks identified in 370 healthy subjects as a template in the analysis of the PD data. Clinical assessment comprised motor features (Movement Disorder Society-Unified Parkinson's Disease Rating Scale; MDS-UPDRS motor scale) and predominantly non-dopaminergic features (SEverity of Non-dopaminergic Symptoms in Parkinson's Disease; SENS-PD scale: postural instability and gait difficulty, psychotic symptoms, excessive daytime sleepiness, autonomic dysfunction, cognitive impairment and depressive symptoms). Voxel-based analyses were performed within networks significantly associated with PD.

RESULTS

The anterior and posterior cingulate network showed decreased integrity, associated with the SENS-PD score, p = 0.001 (β = - 0.265, ηp2 = 0.070) and p = 0.001 (β = - 0.264, ηp2 = 0.074), respectively. Of the components of the SENS-PD score, cognitive impairment and excessive daytime sleepiness were associated with atrophy within both networks.

CONCLUSIONS

We identified loss of integrity and atrophy in the anterior and posterior cingulate networks in PD patients. Abnormalities of both networks were associated with predominantly non-dopaminergic features, specifically cognition and excessive daytime sleepiness. Our findings suggest that (components of) the cingulate networks display a specific vulnerability to the pathobiology of PD and may operate as interfaces between networks involved in cognition and alertness.

Enhanced Feedback-Related Negativity in Alzheimer's Disease

Alzheimer’s disease (AD), the most common cause of dementia in the elderly, results in the impairment of executive function, including that of performance monitoring. Feedback-related negativity (FRN) is an electrophysiological measure reflecting the activity of this monitoring system via feedback signals, and is generated from the anterior cingulate cortex. However, there have been no reports on FRN in AD. Based on prior aging studies, we hypothesized that FRN would decrease in AD patients. To assess this, FRN was measured in healthy individuals and those with AD during a simple gambling task involving positive and negative feedback stimuli. Contrary to our hypothesis, FRN amplitude increased in AD patients, compared with the healthy elderly. We speculate that this may reflect the existence of a compensatory mechanism against the decline in executive function. Also, there was a significant association between FRN amplitude and depression scores in AD, and the FRN amplitude tended to increase insomuch as the Self-rating Depression Scale (SDS) was higher. This result suggests the existence of a negative bias in the affective state in AD. Thus, the impaired functioning monitoring system in AD is a more complex phenomenon than we thought.

Gray matter network differences between behavioral variant frontotemporal dementia and Alzheimer's disease

We set out to study whether single-subject gray matter (GM) networks show disturbances that are specific for Alzheimer's disease (AD; n = 90) or behavioral variant frontotemporal dementia (bvFTD; n = 59), and whether such disturbances would be related to cognitive deficits measured with mini-mental state examination and a neuropsychological battery, using subjective cognitive decline subjects as reference. AD and bvFTD patients had a lower degree, connectivity density, clustering, path length, betweenness centrality, and small world values compared with subjective cognitive decline. AD patients had a lower connectivity density than bvFTD patients (F = 5.79, p = 0.02; mean ± standard deviation bvFTD 16.10 ± 1.19%; mean ± standard deviation AD 15.64 ± 1.02%). Lasso logistic regression showed that connectivity differences between bvFTD and AD were specific to 23 anatomical areas, in terms of local GM volume, degree, and clustering. Lower clustering values and lower degree values were specifically associated with worse mini-mental state examination scores and lower performance on the neuropsychological tests. GM showed disease-specific alterations, when comparing bvFTD with AD patients, and these alterations were associated with cognitive deficits.

Early grey matter changes in structural covariance networks in Huntington's disease

Background

Progressive subcortical changes are known to occur in Huntington's disease (HD), a hereditary neurodegenerative disorder. Less is known about the occurrence and cohesion of whole brain grey matter changes in HD.

Objectives

We aimed to detect network integrity changes in grey matter structural covariance networks and examined relationships with clinical assessments.

Methods

Structural magnetic resonance imaging data of premanifest HD (n = 30), HD patients (n = 30) and controls (n = 30) was used to identify ten structural covariance networks based on a novel technique using the co-variation of grey matter with independent component analysis in FSL. Group differences were studied controlling for age and gender. To explore whether our approach is effective in examining grey matter changes, regional voxel-based analysis was additionally performed.

Results

Premanifest HD and HD patients showed decreased network integrity in two networks compared to controls. One network included the caudate nucleus, precuneous and anterior cingulate cortex (in HD p < 0.001, in pre-HD p = 0.003). One other network contained the hippocampus, premotor, sensorimotor, and insular cortices (in HD p < 0.001, in pre-HD p = 0.023). Additionally, in HD patients only, decreased network integrity was observed in a network including the lingual gyrus, intracalcarine, cuneal, and lateral occipital cortices (p = 0.032). Changes in network integrity were significantly associated with scores of motor and neuropsychological assessments. In premanifest HD, voxel-based analyses showed pronounced volume loss in the basal ganglia, but less prominent in cortical regions.

Conclusion

Our results suggest that structural covariance might be a sensitive approach to reveal early grey matter changes, especially for premanifest HD.

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Identification of anatomical networks in Huntington's disease (HD).

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Independent component analysis was used to examine structural covariance networks.

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HD patients showed changes in subcortical and cortical covariance networks.

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A network-based approach is sensitive to reveal early grey matter changes.

Structural Covariance Networks and Their Association with Age, Features of Cerebral Small-Vessel Disease, and Cognitive Functioning in Older Persons

Recently, cerebral structural covariance networks (SCNs) have been shown to partially overlap with functional networks. However, although for some of these SCNs a strong association with age is reported, less is known about the association of individual SCNs with separate cognition domains and the potential mediation effect in this of cerebral small vessel disease (SVD). In 219 participants (aged 75-96 years) with mild cognitive deficits, 8 SCNs were defined based on structural covariance of gray matter intensity with independent component analysis on 3DT1-weighted magnetic resonance imaging (MRI). Features of SVD included volume of white matter hyperintensities (WMH), lacunar infarcts, and microbleeds. Associations with SCNs were examined with multiple linear regression analyses, adjusted for age and/or gender. In addition to higher age, which was associated with decreased expression of subcortical, premotor, temporal, and occipital-precuneus networks, the presence of SVD and especially higher WMH volume was associated with a decreased expression in the occipital, cerebellar, subcortical, and anterior cingulate network. The temporal network was associated with memory (p = 0.005), whereas the cerebellar-occipital and occipital-precuneus networks were associated with psychomotor speed (p = 0.002 and p < 0.001). Our data show that a decreased expression of specific networks, including the temporal and occipital lobe and cerebellum, was related to decreased cognitive functioning, independently of age and SVD. This indicates the potential of SCNs in substantiating cognitive functioning in older persons.