Resting-state connectivity in neurodegenerative disorders: Is there potential for an imaging biomarker?

PET-based brain molecular connectivity in neurodegenerative disease

PURPOSE OF REVIEW

Molecular imaging has traditionally been used and interpreted primarily in the context of localized and relatively static neurochemical processes. New understanding of brain function and development of novel molecular imaging protocols and analysis methods highlights the relevance of molecular networks that co-exist and interact with functional and structural networks. Although the concept and evidence of disease-specific metabolic brain patterns has existed for some time, only recently has such an approach been applied in the neurotransmitter domain and in the context of multitracer and multimodal studies. This review briefly summarizes initial findings and highlights emerging applications enabled by this new approach.

RECENT FINDINGS

Connectivity based approaches applied to molecular and multimodal imaging have uncovered molecular networks with neurodegeneration-related alterations to metabolism and neurotransmission that uniquely relate to clinical findings; better disease stratification paradigms; an improved understanding of the relationships between neurochemical and functional networks and their related alterations, although the directionality of these relationships are still unresolved; and a new understanding of the molecular underpinning of disease-related alteration in resting-state brain activity.

SUMMARY

Connectivity approaches are poised to greatly enhance the information that can be extracted from molecular imaging. While currently mostly contributing to enhancing understanding of brain function, they are highly likely to contribute to the identification of specific biomarkers that will improve disease management and clinical care.

Resting-state networks and their relationship with MoCA performance in PD patients

Although mild cognitive impairment is a common non-motor symptom experienced by individuals with Parkinson's Disease, the changes in intrinsic resting-state networks associated with its onset in Parkinson's remain underexamined. To address the issue, our study sought to examine resting-state network alterations and their association with total performance in the Montreal Cognitive Assessment and its cognitive domains in Parkinson's by means of functional magnetic resonance imaging of 29 Parkinson's patients with normal cognition, 25 Parkinson's patients with mild cognitive impairment, and 13 healthy controls. To contrast the Parkinson's groups with each other and the controls, the images were used to estimate the Z-score coefficient between the regions of interest from the default mode network, the salience network and the central executive network. Our first finding was that default mode and salience network connectivity decreased significantly in Parkinson's patients regardless of their cognitive status. Additionally, default mode network nodes had a negative and salience network nodes a positive correlation with the global assessment in Parkinson's with normal cognition; this inverse relationship of both networks to total score was not found in the group with cognitive impairment. Finally, a positive correlation was found between executive scores and anterior and posterior cortical network connectivity and, in the group with cognitive impairment, between language scores and salience network connectivity. Our results suggest that specific resting-state networks of Parkinson's patients with cognitive impairment differ from those of Parkinson's patients with normal cognition, supporting the evidence that cognitive impairment in Parkinson's Disease displays a differentiated neurodegenerative pattern.

Predictive Models for the Transition from Mild Neurocognitive Disorder to Major Neurocognitive Disorder: Insights from Clinical, Demographic, and Neuropsychological Data

Neurocognitive disorders (NCDs) are progressive conditions that severely impact cognitive function and daily living. Understanding the transition from mild to major NCD is crucial for personalized early intervention and effective management. Predictive models incorporating demographic variables, clinical data, and scores on neuropsychological and emotional tests can significantly enhance early detection and intervention strategies in primary healthcare settings. We aimed to develop and validate predictive models for the progression from mild NCD to major NCD using demographic, clinical, and neuropsychological data from 132 participants over a two-year period. Generalized Estimating Equations were employed for data analysis. Our final model achieved an accuracy of 83.7%. A higher body mass index and alcohol drinking increased the risk of progression from mild NCD to major NCD, while female sex, higher praxis abilities, and a higher score on the Geriatric Depression Scale reduced the risk. Here, we show that integrating multiple factors-ones that can be easily examined in clinical settings-into predictive models can improve early diagnosis of major NCD. This approach could facilitate timely interventions, potentially mitigating the progression of cognitive decline and improving patient outcomes in primary healthcare settings. Further research should focus on validating these models across diverse populations and exploring their implementation in various clinical contexts.

Detecting Alzheimer's Disease Stages and Frontotemporal Dementia in Time Courses of Resting-State fMRI Data Using a Machine Learning Approach

Early, accurate diagnosis of neurodegenerative dementia subtypes such as Alzheimer's disease (AD) and frontotemporal dementia (FTD) is crucial for the effectiveness of their treatments. However, distinguishing these conditions becomes challenging when symptoms overlap or the conditions present atypically. Resting-state fMRI (rs-fMRI) studies have demonstrated condition-specific alterations in AD, FTD, and mild cognitive impairment (MCI) compared to healthy controls (HC). Here, we used machine learning to build a diagnostic classification model based on these alterations. We curated all rs-fMRIs and their corresponding clinical information from the ADNI and FTLDNI databases. Imaging data underwent preprocessing, time course extraction, and feature extraction in preparation for the analyses. The imaging features data and clinical variables were fed into gradient-boosted decision trees with fivefold nested cross-validation to build models that classified four groups: AD, FTD, HC, and MCI. The mean and 95% confidence intervals for model performance metrics were calculated using the unseen test sets in the cross-validation rounds. The model built using only imaging features achieved 74.4% mean balanced accuracy, 0.94 mean macro-averaged AUC, and 0.73 mean macro-averaged F1 score. It accurately classified FTD (F1 = 0.99), HC (F1 = 0.99), and MCI (F1 = 0.86) fMRIs but mostly misclassified AD scans as MCI (F1 = 0.08). Adding clinical variables to model inputs raised balanced accuracy to 91.1%, macro-averaged AUC to 0.99, macro-averaged F1 score to 0.92, and improved AD classification accuracy (F1 = 0.74). In conclusion, a multimodal model based on rs-fMRI and clinical data accurately differentiates AD-MCI vs. FTD vs. HC.

Encoding Manual Dexterity through Modulation of Intrinsic α Band Connectivity

The human hand possesses both consolidated motor skills and remarkable flexibility in adapting to ongoing task demands. However, the underlying mechanisms by which the brain balances stability and flexibility remain unknown. In the absence of external input or behavior, spontaneous (intrinsic) brain connectivity is thought to represent a prior of stored memories. In this study, we investigated how manual dexterity modulates spontaneous functional connectivity in the motor cortex during hand movement. Using magnetoencephalography, in 47 human participants (both sexes), we examined connectivity modulations in the α and β frequency bands at rest and during two motor tasks (i.e., finger tapping or toe squeezing). The flexibility and stability of such modulations allowed us to identify two groups of participants with different levels of performance (high and low performers) on the nine-hole peg test, a test of manual dexterity. In the α band, participants with higher manual dexterity showed distributed decreases of connectivity, specifically in the motor cortex, increased segregation, and reduced nodal centrality. Participants with lower manual dexterity showed an opposite pattern. Notably, these patterns from the brain to behavior are mirrored by results from behavior to the brain. Indeed, when participants were divided using the median split of the dexterity score, we found the same connectivity patterns. In summary, this experiment shows that a long-term motor skill-manual dexterity-influences the way the motor systems respond during movements.

Dysregulation of extracellular potassium distinguishes healthy ageing from neurodegeneration

Progressive neuronal loss is a hallmark feature distinguishing neurodegenerative diseases from normal ageing. However, the underlying mechanisms remain unknown. Extracellular K+ homeostasis is a potential mediator of neuronal injury as K+ elevations increase excitatory activity. The dysregulation of extracellular K+ and potassium channel expressions during neurodegeneration could contribute to this distinction. Here we measured the cortical extracellular K+ concentration ([K+]e) in awake wild-type mice as well as murine models of neurodegeneration using K+-sensitive microelectrodes. Unexpectedly, aged wild-type mice exhibited significantly lower cortical [K+]e than young mice. In contrast, cortical [K+]e was consistently elevated in Alzheimer's disease (APP/PS1), amyotrophic lateral sclerosis (ALS) (SOD1G93A) and Huntington's disease (R6/2) models. Cortical resting [K+]e correlated inversely with neuronal density and the [K+]e buffering rate but correlated positively with the predicted neuronal firing rate. Screening of astrocyte-selective genomic datasets revealed a number of potassium channel genes that were downregulated in these disease models but not in normal ageing. In particular, the inwardly rectifying potassium channel Kcnj10 was downregulated in ALS and Huntington's disease models but not in normal ageing, while Fxyd1 and Slc1a3, each of which acts as a negative regulator of potassium uptake, were each upregulated by astrocytes in both Alzheimer's disease and ALS models. Chronic elevation of [K+]e in response to changes in gene expression and the attendant neuronal hyperexcitability may drive the neuronal loss characteristic of these neurodegenerative diseases. These observations suggest that the dysregulation of extracellular K+ homeostasis in a number of neurodegenerative diseases could be due to aberrant astrocytic K+ buffering and as such, highlight a fundamental role for glial dysfunction in neurodegeneration.

Corneal confocal microscopy may help to distinguish Multiple System Atrophy from Parkinson's disease

Multiple system atrophy (MSA) and Parkinson's disease (PD) have clinical overlapping symptoms, which makes differential diagnosis difficult. Our research aimed to distinguish MSA from PD using corneal confocal microscopy (CCM), a noninvasive and objective test. The study included 63 PD patients, 30 MSA patients, and 31 healthy controls (HC). When recruiting PD and MSA, questionnaires were conducted on motor and non-motor functions, such as autonomic and cognitive functions. Participants underwent CCM to quantify the corneal nerve fibers. Corneal nerve fiber density (CNFD) and corneal nerve fiber length (CNFL) values in MSA are lower than PD (MSA vs. PD: CNFD, 20.68 ± 6.70 vs. 24.64 ± 6.43 no./mm2, p < 0.05; CNFL, 12.01 ± 3.25 vs. 14.17 ± 3.52 no./mm2, p < 0.05). In MSA + PD (combined), there is a negative correlation between CNFD and the Orthostatic Grading Scale (OGS) (r = -0.284, p = 0.007). Similarly, CNFD in the only MSA group was negatively correlated with the Unified Multiple System Atrophy Rating Scale I and II (r = -0.391, p = 0.044; r = -0.382, p = 0.049). CNFD and CNFL were inversely associated with MSA (CNFD: β = -0.071; OR, 0.932; 95% CI, 0.872 ~ 0.996; p = 0.038; CNFL: β = -0.135; OR, 0.874; 95% CI, 0.768-0.994; p = 0.040). Furthermore, we found the area under the receiver operating characteristic curve (ROC) of CNFL was the largest, 72.01%. The CCM could be an objective and sensitive biomarker to distinguish MSA from PD. It visually reflects a more severe degeneration in MSA compared to PD.

Neurovascular coupling alteration in drug-naïve Parkinson's disease: The underlying molecular mechanisms and levodopa's restoration effects

BACKGROUND

Parkinson's disease (PD) patients exhibit an imbalance between neuronal activity and perfusion, referred to as abnormal neurovascular coupling (NVC). Nevertheless, the underlying molecular mechanism and how levodopa, the standard treatment in PD, regulates NVC is largely unknown.

MATERIAL AND METHODS

A total of 52 drug-naïve PD patients and 49 normal controls (NCs) were enrolled. NVC was characterized in vivo by relating cerebral blood flow (CBF) and amplitude of low-frequency fluctuations (ALFF). Motor assessments and MRI scanning were conducted on drug-naïve patients before and after levodopa therapy (OFF/ON state). Regional NVC differences between patients and NCs were identified, followed by an assessment of the associated receptors/transporters. The influence of levodopa on NVC, CBF, and ALFF within these abnormal regions was analyzed.

RESULTS

Compared to NCs, OFF-state patients showed NVC dysfunction in significantly lower NVC in left precentral, postcentral, superior parietal cortex, and precuneus, along with higher NVC in left anterior cingulate cortex, right olfactory cortex, thalamus, caudate, and putamen (P-value <0.0006). The distribution of NVC differences correlated with the density of dopaminergic, serotonin, MU-opioid, and cholinergic receptors/transporters. Additionally, levodopa ameliorated abnormal NVC in most of these regions, where there were primarily ALFF changes with limited CBF modifications.

CONCLUSION

Patients exhibited NVC dysfunction primarily in the striato-thalamo-cortical circuit and motor control regions, which could be driven by dopaminergic and nondopaminergic systems, and levodopa therapy mainly restored abnormal NVC by modulating neuronal activity.

Structural, static, and dynamic functional MRI predictors for conversion from mild cognitive impairment to Alzheimer's disease: Inter-cohort validation of Shanghai Memory Study and ADNI

Mild cognitive impairment (MCI) is a critical prodromal stage of Alzheimer's disease (AD), and the mechanism underlying the conversion is not fully explored. Construction and inter-cohort validation of imaging biomarkers for predicting MCI conversion is of great challenge at present, due to lack of longitudinal cohorts and poor reproducibility of various study-specific imaging indices. We proposed a novel framework for inter-cohort MCI conversion prediction, involving comparison of structural, static, and dynamic functional brain features from structural magnetic resonance imaging (sMRI) and resting-state functional MRI (fMRI) between MCI converters (MCI_C) and non-converters (MCI_NC), and support vector machine for construction of prediction models. A total of 218 MCI patients with 3-year follow-up outcome were selected from two independent cohorts: Shanghai Memory Study cohort for internal cross-validation, and Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort for external validation. In comparison with MCI_NC, MCI_C were mainly characterized by atrophy, regional hyperactivity and inter-network hypo-connectivity, and dynamic alterations characterized by regional and connectional instability, involving medial temporal lobe (MTL), posterior parietal cortex (PPC), and occipital cortex. All imaging-based prediction models achieved an area under the curve (AUC) > 0.7 in both cohorts, with the multi-modality MRI models as the best with excellent performances of AUC > 0.85. Notably, the combination of static and dynamic fMRI resulted in overall better performance as relative to static or dynamic fMRI solely, supporting the contribution of dynamic features. This inter-cohort validation study provides a new insight into the mechanisms of MCI conversion involving brain dynamics, and paves a way for clinical use of structural and functional MRI biomarkers in future.

A systematic literature review of fMRI and EEG resting-state functional connectivity in Dementia with Lewy Bodies: Underlying mechanisms, clinical manifestation, and methodological considerations

Previous studies suggest that there may be important links between functional connectivity, disease mechanisms underpinning the Dementia with Lewy Body (DLB) and the key clinical symptoms, but the exact relationship remains unclear. We performed a systematic literature review to address this gap by summarising the research findings while critically considering the impact of methodological differences on findings. The main methodological choices of fMRI articles included data-driven, seed-based or regions of interest approaches, or their combinations. Most studies focused on examining large-scale resting-state networks, which revealed a consistent decrease in connectivity and some associations with non-cognitive symptoms. Although the inter-network connectivity showed mixed results, the main finding is consistent with theories positing disconnection between visual and attentional areas of the brain implicated in the aetiology of psychotic symptoms in the DLB. The primary methodological choice of EEG studies was implementing the phase lag index and using graph theory. The EEG studies revealed a consistent decrease in connectivity on alpha and beta frequency bands. While the overall trend of findings showed decreased connectivity, more subtle changes in the directionality of connectivity were observed when using a hypothesis-driven approach. Problems with cognition were also linked with greater functional connectivity disturbances. In summary, connectivity measures can capture brain disturbances in the DLB and remain crucial in uncovering the causal relationship between the networks' disorganisation and underlying mechanisms resulting in psychotic, motor, and cognitive symptoms of the DLB.

Bradykinesia and rigidity modulated by functional connectivity between the primary motor cortex and globus pallidus in Parkinson's disease

The mechanisms underlying motor fluctuations in patients with Parkinson's disease (PD) are currently unclear. Regional brain stimulation reported the changing of motor symptoms, but the correlation with functional connectivity (FC) in the brain network is not fully understood. Hence, our study aimed to explore the relationship between motor symptom severity and FC using resting-state functional magnetic resonance imaging (rsfMRI) in the "on" and "off" states of PD. In 26 patients with sporadic PD, FC was assessed using rsfMRI, and clinical severity was analyzed using the motor part of the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS Part III) in the on and off states. Correlations between FC values and MDS-UPDRS Part III scores were assessed using Pearson's correlation coefficient. The correlation between FC and motor symptoms differed in the on and off states. FC between the ipsilateral precentral gyrus (PreCG) and globus pallidus (GP) correlated with the total MDS-UPDRS Part III scores and those for bradykinesia/rigidity in the off state. Lateralization analysis indicated that FC between the PreCG and GP correlated with the contralateral total MDS-UPDRS Part III scores and those for bradykinesia/rigidity in the off state. Aberrant FC in cortico-striatal circuits correlated with the severity of motor symptoms in PD. Cortico-striatal hyperconnectivity, particularly in motor pathways involving PreCG and GP, is related to motor impairments in PD. These findings may facilitate our understanding of the mechanisms underlying motor symptoms in PD and aid in developing treatment strategies such as brain stimulation for motor impairment.

Functional disruptions of the brain network in low back pain: a graph-theoretical study

PURPOSE

The aim of this study was to investigate alterations in the topological organization of whole-brain functional networks in patients with chronic low back pain (CLBP) and characterize the relationship of these alterations with pain characteristics.

METHODS

Thirty-three CLBP patients and 34 matched healthy controls (HCs) underwent fMRI scans. A graph-theoretical approach was applied to identify brain network changes in patients suffering from chronic low back pain given its nonspecific etiology and complexity. Graph theory-based analysis was used to construct functional connectivity matrices and extract the features of small-world networks of the brain in both groups. Then, the whole-brain functional connectivity differences were characterized by network-based statistics (NBS) analysis, and the relationship between the altered brain features and clinical measures was explored.

RESULTS

At the global level, patients with CLBP showed significantly decreased gamma, sigma, global efficiency, and local efficiency and increased lambda and shortest path length compared with HCs. At the regional level, there were deficits in nodal efficiency within the default mode network and salience network. NBS analysis demonstrated that decreased functional connectivity was present in the CLBP patients, mainly in the frontolimbic circuit and temporal regions. Furthermore, aspects of topological dysfunctions in CLBP were correlated with pain severity.

CONCLUSION

This study highlighted the aberrant topological organization of functional brain networks in CLBP, which may shed light on the pathophysiology of CLBP and support the development of pain management approaches.

Connectome-based fingerprint of motor impairment is stable along the course of Parkinson's disease

Functional alterations in brain connectivity have previously been described in Parkinson's disease, but it is not clear whether individual differences in connectivity profiles might be also linked to severity of motor-symptom manifestation. Here we investigated the relevance of individual functional connectivity patterns measured with resting-state fMRI with respect to motor-symptom severity in Parkinson's disease, through a whole-brain, data-driven approach (connectome-based predictive modeling). Neuroimaging and clinical data of Parkinson's disease patients from the Parkinson's Progression Markers Initiative were derived at baseline (session 1, n = 81) and at follow-up (session 2, n = 53). Connectome-based predictive modeling protocol was implemented to predict levels of motor impairment from individual connectivity profiles. The resulting predictive model comprised a network mainly involving functional connections between regions located in the cerebellum, and in the motor and frontoparietal networks. The predictive power of the model was stable along disease progression, as the connectivity within the same network could predict levels of motor impairment, even at a later stage of the disease. Finally, connectivity profiles within this network could be identified at the individual level, suggesting the presence of individual fingerprints within resting-state fMRI connectivity associated with motor manifestations in Parkinson's disease.

Virtual brain simulations reveal network-specific parameters in neurodegenerative dementias

Introduction

Neural circuit alterations lay at the core of brain physiopathology, and yet are hard to unveil in living subjects. The Virtual Brain (TVB) modeling, by exploiting structural and functional magnetic resonance imaging (MRI), yields mesoscopic parameters of connectivity and synaptic transmission.

Methods

We used TVB to simulate brain networks, which are key for human brain function, in Alzheimer's disease (AD) and frontotemporal dementia (FTD) patients, whose connectivity and synaptic parameters remain largely unknown; we then compared them to healthy controls, to reveal novel in vivo pathological hallmarks.

Results

The pattern of simulated parameter differed between AD and FTD, shedding light on disease-specific alterations in brain networks. Individual subjects displayed subtle differences in network parameter patterns that significantly correlated with their individual neuropsychological, clinical, and pharmacological profiles.

Discussion

These TVB simulations, by informing about a new personalized set of networks parameters, open new perspectives for understanding dementias mechanisms and design personalized therapeutic approaches.

Intrinsic brain activity alterations in patients with Parkinson's disease

OBJECTIVE

The objective of this study is to explore the brain activity alterations in Parkinson's disease (PD) from the perspectives of neuronal activity, synchronization of neuronal activity, and coordination of whole-brain activity.

METHODS

In this study, we recruited 38 PD patients and 35 matched healthy controls (HCs). We explored intrinsic brain activity alterations in PD by comparing resting-state functional magnetic resonance imaging (rs-fMRI) metrics of the amplitude of low-frequency of fluctuation (ALFF), the fractional amplitude of low-frequency fluctuation (fALFF), percent amplitude of fluctuation (PerAF), regional homogeneity (ReHo), and degree centrality (DC). Two-sample t-tests were used to determine the differences between the two groups. Spearman correlation analysis was used to explore the relationships between abnormal ALFF, fALFF, PerAF, ReHo, and DC values and clinical indicators such as the Movement Disorder Society's Unified Parkinson's Disease Rating Scale (MDS-UPDRS), Hoehn and Yahr (H&Y) stage, and duration of disease.

RESULTS

Compared with the HCs, PD had increased ALFF,fALFF, and PerAF in the temporal lobe and cerebellum, and decreased ALFF,fALFF, and PerAF in the occipital-parietal lobe in the neuronal activity. In the synchronization of neuronal activity, PD patients had increased ReHo in the right inferior parietal lobule and decreased ReHo in the caudate. In the coordination of whole-brain activity, PD patients had increased DC in the cerebellum and decreased DC in the occipital lobe. Correlation analysis showed that there is a correlation between abnormal brain regions and clinical indicators in PD. Notably, the changes in occipital lobe brain activity were found in ALFF, fALFF, PerAF, and DC, and were most correlated with the clinical indicators of PD patients.

CONCLUSIONS

This study found that intrinsic brain function in several occipital-temporal-parietal and cerebellum regions was altered in PD patients, potentially related to the clinical indicators of PD. These results may enhance our understanding of the underlying neural mechanisms of PD and may contribute to further exploring the selection of therapeutic targets in PD patients.

Effects of spaceflight on the EEG alpha power and functional connectivity

Electroencephalography (EEG) can detect changes in cerebral activity during spaceflight. This study evaluates the effect of spaceflight on brain networks through analysis of the Default Mode Network (DMN)'s alpha frequency band power and functional connectivity (FC), and the persistence of these changes. Five astronauts' resting state EEGs under three conditions were analyzed (pre-flight, in-flight, and post-flight). DMN's alpha band power and FC were computed using eLORETA and phase-locking value. Eyes-opened (EO) and eyes-closed (EC) conditions were differentiated. We found a DMN alpha band power reduction during in-flight (EC: p < 0.001; EO: p < 0.05) and post-flight (EC: p < 0.001; EO: p < 0.01) when compared to pre-flight condition. FC strength decreased during in-flight (EC: p < 0.01; EO: p < 0.01) and post-flight (EC: ns; EO: p < 0.01) compared to pre-flight condition. The DMN alpha band power and FC strength reduction persisted until 20 days after landing. Spaceflight caused electrocerebral alterations that persisted after return to earth. Periodic assessment by EEG-derived DMN analysis has the potential to become a neurophysiologic marker of cerebral functional integrity during exploration missions to space.

Whole-brain mapping of effective connectivity by fMRI with cortex-wide patterned optogenetics

Functional magnetic resonance imaging (fMRI) with optogenetic neural manipulation is a powerful tool that enables brain-wide mapping of effective functional networks. To achieve flexible manipulation of neural excitation throughout the mouse cortex, we incorporated spatiotemporal programmable optogenetic stimuli generated by a digital micromirror device into an MRI scanner via an optical fiber bundle. This approach offered versatility in space and time in planning the photostimulation pattern, combined with in situ optical imaging and cell-type-specific or circuit-specific genetic targeting in individual mice. Brain-wide effective connectivity obtained by fMRI with optogenetic stimulation of atlas-based cortical regions is generally congruent with anatomically defined axonal tracing data but is affected by the types of anesthetics that act selectively on specific connections. fMRI combined with flexible optogenetics opens a new path to investigate dynamic changes in functional brain states in the same animal through high-throughput brain-wide effective connectivity mapping.

BolT: Fused window transformers for fMRI time series analysis

Deep-learning models have enabled performance leaps in analysis of high-dimensional functional MRI (fMRI) data. Yet, many previous methods are suboptimally sensitive for contextual representations across diverse time scales. Here, we present BolT, a blood-oxygen-level-dependent transformer model, for analyzing multi-variate fMRI time series. BolT leverages a cascade of transformer encoders equipped with a novel fused window attention mechanism. Encoding is performed on temporally-overlapped windows within the time series to capture local representations. To integrate information temporally, cross-window attention is computed between base tokens in each window and fringe tokens from neighboring windows. To gradually transition from local to global representations, the extent of window overlap and thereby number of fringe tokens are progressively increased across the cascade. Finally, a novel cross-window regularization is employed to align high-level classification features across the time series. Comprehensive experiments on large-scale public datasets demonstrate the superior performance of BolT against state-of-the-art methods. Furthermore, explanatory analyses to identify landmark time points and regions that contribute most significantly to model decisions corroborate prominent neuroscientific findings in the literature.

Reliability of resting-state functional connectivity in the human spinal cord: assessing the impact of distinct noise sources

The investigation of spontaneous fluctuations of the blood-oxygen-level-dependent (BOLD) signal has recently been extended from the brain to the spinal cord, where it has stimulated interest from a clinical perspective. A number of resting-state functional magnetic resonance imaging (fMRI) studies have demonstrated robust functional connectivity between the time series of BOLD fluctuations in bilateral dorsal horns and between those in bilateral ventral horns, in line with the functional neuroanatomy of the spinal cord. A necessary step prior to extension to clinical studies is assessing the reliability of such resting-state signals, which we aimed to do here in a group of 45 healthy young adults at the clinically prevalent field strength of 3T. When investigating connectivity in the entire cervical spinal cord, we observed fair to good reliability for dorsal-dorsal and ventral-ventral connectivity, whereas reliability was poor for within- and between-hemicord dorsal-ventral connectivity. Considering how prone spinal cord fMRI is to noise, we extensively investigated the impact of distinct noise sources and made two crucial observations: removal of physiological noise led to a reduction in functional connectivity strength and reliability - due to the removal of stable and participant-specific noise patterns - whereas removal of thermal noise considerably increased the detectability of functional connectivity without a clear influence on reliability. Finally, we also assessed connectivity within spinal cord segments and observed that while the pattern of connectivity was similar to that of whole cervical cord, reliability at the level of single segments was consistently poor. Taken together, our results demonstrate the presence of reliable resting-state functional connectivity in the human spinal cord even after thoroughly accounting for physiological and thermal noise, but at the same time urge caution if focal changes in connectivity (e.g. due to segmental lesions) are to be studied, especially in a longitudinal manner.

Dysconnection and cognition in schizophrenia: A spectral dynamic causal modeling study

Schizophrenia (SZ) is a severe mental disorder characterized by failure of functional integration (aka dysconnection) across the brain. Recent functional connectivity (FC) studies have adopted functional parcellations to define subnetworks of large-scale networks, and to characterize the (dys)connection between them, in normal and clinical populations. While FC examines statistical dependencies between observations, model-based effective connectivity (EC) can disclose the causal influences that underwrite the observed dependencies. In this study, we investigated resting state EC within seven large-scale networks, in 66 SZ and 74 healthy subjects from a public dataset. The results showed that a remarkable 33% of the effective connections (among subnetworks) of the cognitive control network had been pathologically modulated in SZ. Further dysconnection was identified within the visual, default mode and sensorimotor networks of SZ subjects, with 24%, 20%, and 11% aberrant couplings. Overall, the proportion of discriminative connections was remarkably larger in EC (24%) than FC (1%) analysis. Subsequently, to study the neural correlates of impaired cognition in SZ, we conducted a canonical correlation analysis between the EC parameters and the cognitive scores of the patients. As such, the self-inhibitions of supplementary motor area and paracentral lobule (in the sensorimotor network) and the excitatory connection from parahippocampal gyrus to inferior temporal gyrus (in the cognitive control network) were significantly correlated with the social cognition, reasoning/problem solving and working memory capabilities of the patients. Future research can investigate the potential of whole-brain EC as a biomarker for diagnosis of brain disorders and for neuroimaging-based cognitive assessment.

Functional connectome automatically differentiates multiple system atrophy (parkinsonian type) from idiopathic Parkinson's disease at early stages

Differentiating the parkinsonian variant of multiple system atrophy (MSA-P) from idiopathic Parkinson's disease (IPD) is challenging, especially in the early stages. This study aimed to investigate differences and similarities in the brain functional connectomes of IPD and MSA-P patients and use machine learning methods to explore the diagnostic utility of these features. Resting-state fMRI data were acquired from 88 healthy controls, 76 MSA-P patients, and 53 IPD patients using a 3.0 T scanner. The whole-brain functional connectome was constructed by thresholding the Pearson correlation matrices of 116 regions, and topological properties were evaluated through graph theory approaches. Connectome measurements were used as features in machine learning models (random forest [RF]/logistic regression [LR]/support vector machine) to distinguish IPD and MSA-P patients. Regarding graph metrics, early IPD and MSA-P patients shared network topological properties. Both patient groups showed functional connectivity disruptions within the cerebellum-basal ganglia-cortical network, but these disconnections were mainly in the cortico-thalamo-cerebellar circuits in MSA-P patients and the basal ganglia-thalamo-cortical circuits in IPD patients. Among the connectome parameters, t tests combined with the RF method identified 15 features, from which the LR classifier achieved the best diagnostic performance on the validation set (accuracy = 92.31%, sensitivity = 90.91%, specificity = 93.33%, area under the receiver operating characteristic curve = 0.89). MSA-P and IPD patients show similar whole-brain network topological alterations. MSA-P primarily affects cerebellar nodes, and IPD primarily affects basal ganglia nodes; both conditions disrupt the cerebellum-basal ganglia-cortical network. Moreover, functional connectome parameters showed outstanding value in the differential diagnosis of early MSA-P and IPD.

Characterization of regional differences in resting-state fMRI with a data-driven network model of brain dynamics

Model-based data analysis of whole-brain dynamics links the observed data to model parameters in a network of neural masses. Recently, studies focused on the role of regional variance of model parameters. Such analyses however necessarily depend on the properties of preselected neural mass model. We introduce a method to infer from the functional data both the neural mass model representing the regional dynamics and the region- and subject-specific parameters while respecting the known network structure. We apply the method to human resting-state fMRI. We find that the underlying dynamics can be described as noisy fluctuations around a single fixed point. The method reliably discovers three regional parameters with clear and distinct role in the dynamics, one of which is strongly correlated with the first principal component of the gene expression spatial map. The present approach opens a novel way to the analysis of resting-state fMRI with possible applications for understanding the brain dynamics during aging or neurodegeneration.

Review of Technological Challenges in Personalised Medicine and Early Diagnosis of Neurodegenerative Disorders

Neurodegenerative disorders are characterised by progressive neuron loss in specific brain areas. The most common are Alzheimer's disease and Parkinson's disease; in both cases, diagnosis is based on clinical tests with limited capability to discriminate between similar neurodegenerative disorders and detect the early stages of the disease. It is common that by the time a patient is diagnosed with the disease, the level of neurodegeneration is already severe. Thus, it is critical to find new diagnostic methods that allow earlier and more accurate disease detection. This study reviews the methods available for the clinical diagnosis of neurodegenerative diseases and potentially interesting new technologies. Neuroimaging techniques are the most widely used in clinical practice, and new techniques such as magnetic resonance imaging (MRI) and positron emission tomography (PET) have significantly improved the diagnosis quality. Identifying biomarkers in peripheral samples such as blood or cerebrospinal fluid is a major focus of the current research on neurodegenerative diseases. The discovery of good markers could allow preventive screening to identify early or asymptomatic stages of the neurodegenerative process. These methods, in combination with artificial intelligence, could contribute to the generation of predictive models that will help clinicians in the early diagnosis, stratification, and prognostic assessment of patients, leading to improvements in patient treatment and quality of life.

Hippocampal, basal ganglia and olfactory connectivity contribute to cognitive impairments in Parkinson's disease

Cognitive impairment is increasingly recognized as a characteristic feature of Parkinson's disease (PD), yet relatively little is known about its underlying neurobiology. Previous investigations suggest that dementia in PD is associated with subcortical atrophy, but similar studies in PD with mild cognitive impairment have been mixed. Variability in cognitive phenotypes and diversity of PD symptoms suggest that a common neuropathological origin results in a multitude of impacts within the brain. These direct and indirect impacts of disease pathology can be investigated using network analysis. Functional connectivity, for instance, may be more sensitive than atrophy to decline in specific cognitive domains in the PD population. Fifty-eight participants with PD underwent a neuropsychological test battery and scanning with structural and resting state functional MRI in a comprehensive whole-brain association analysis. To investigate atrophy as a potential marker of impairment, structural gray matter atrophy was associated with cognitive scores in each cognitive domain using voxel-based morphometry. To investigate connectivity, large-scale networks were correlated with voxel time series and associated with cognitive scores using distance covariance. Structural atrophy was not associated with any cognitive domain, with the exception of visuospatial measures in primary sensory and motor cortices. In contrast, functional connectivity was associated with attention, executive function, language, learning and memory, visuospatial, and global cognition in the bilateral hippocampus, left putamen, olfactory cortex, and bilateral anterior temporal poles. These preliminary results suggest that cognitive domain-specific networks in PD are distinct from each other and could provide a network signature for different cognitive phenotypes.

Altered intrinsic brain activity and cognitive impairment in euthymic, unmedicated individuals with bipolar disorder

Cognitive impairment in euthymic bipolar disorder (BD) contributes to poor functional outcomes. Resting-state magnetic resonance imaging (MRI)may help us understand the neurobiology of cognitive impairment in BD. Here, forty unmedicated euthymic BD patients and thirty-nine healthy controls were recruited, undergoing MRI scans and neuropsychological measures. The amplitude of low-frequency fluctuation (ALFF) and ALFF-based functional connectivity (FC) analysis was employed to explore the potential alterations of neural activity. Voxel-wised correlation was calculated between clinical and cognitive variables and abnormal brain activity. Compared with healthy controls, euthymic BD patients showed worse cognitive performance in Trail Making Test, Digit Span Test, and Stroop Color-Word Test (SCWT). The euthymic BD group had significantly lower ALFF in the left medial frontal gyrus, right middle frontal gyrus, right postcentral gyrus, and left superior frontal gyrus. Furthermore, we found decreased ALFF values in the right middle frontal gyrus that was negatively correlated with cognitive inhibition, (r = -0.43, P = 0.015). ALFF-based FC analysis showed that BD group showed significantly decreased FC between the right middle frontal gyrus (seed) and left middle temporal gyrus and left medial frontal gyrus, (Two-tailed, P_FWE < 0.05, TFCE corrected). The findings demonstrated that individuals with BD during the euthymic phase exhibited decreased ALFF and hypoconnectivity of key brain areas within the frontoparietal network. These altered spontaneous brain activity in euthymic BD patients may be involved in the pathophysiology mechanism of cognitive deficits.

Are the EEG microstates correlated with motor and non-motor parameters in patients with Parkinson's disease?

OBJECTIVES

This study compared electroencephalography microstates (EEG-MS) of patients with Parkinson's disease (PD) to healthy controls and correlated EEG-MS with motor and non-motor aspects of PD.

METHODS

This cross-sectional exploratory study was conducted with patients with PD (n = 10) and healthy controls (n = 10) matched by sex and age. We recorded EEG-MS using 32 channels during eyes-closed and eyes-open conditions and analyzed the four classic EEG-MS maps (A, B, C, D). Clinical information (e.g., disease duration, medications, levodopa equivalent daily dose), motor (Movement Disorder Society - Unified Parkinson Disease Rating Scale II and III, Timed Up and Go simple and dual-task, and Mini-Balance Evaluation Systems Test) and non-motor aspects (Mini-Mental State Exam [MMSE], verbal fluency, Hospital Anxiety and Depression Scale, and Parkinson's Disease Questionnaire-39 [PDQ-39]) were assessed in the PD group. Mann-Whitney U test was used to compare groups, and Spearman's correlation coefficient to analyze the correlations between coverage of EEG-MS and clinical aspects of PD.

RESULTS

The PD group showed a shorter duration of EEG-MS C in the eyes-closed condition than the control group. We observed correlations (rho = 0.64 to 0.82) between EEG-MS B, C, and D and non-motor aspects of PD (MMSE, verbal fluency, PDQ-39, and levodopa equivalent daily dose).

CONCLUSION

Alterations in EEG-MS and correlations between topographies and cognitive aspects, quality of life, and medication dose indicate that EEG could be used as a PD biomarker. Future studies should investigate these associations using a longitudinal design.

Resting-state functional-MRI in iNPH: can default mode and motor networks changes improve patient selection and outcome? Preliminary report

BACKGROUND

Idiopathic normal pressure hydrocephalus (iNPH) is a progressive and partially reversible form of dementia, characterized by impaired interactions between multiple brain regions. Because of the presence of comorbidities and a lack of accurate diagnostic and prognostic biomarkers, only a minority of patients receives disease-specific treatment. Recently, resting-state functional-magnetic resonance imaging (rs-fMRI) has demonstrated functional connectivity alterations in inter-hemispheric, frontal, occipital, default-mode (DMN) and motor network (MN) circuits. Herein, we report our experience in a cohort of iNPH patients that underwent cerebrospinal fluid (CSF) dynamics evaluation and rs-fMRI. The study aimed to identify functional circuits related to iNPH and explore the relationship between DMN and MN recordings and clinical modifications before and after infusion and tap test, trying to understand iNPH pathophysiology and to predict the best responders to ventriculoperitoneal shunt (VPS) implant.

METHODS

We prospectively collected data regarding clinical assessment, neuroradiological findings, lumbar infusion and tap test of thirty-two iNPH patients who underwent VPS implant. Rs-fMRI was performed using MELODIC-ICA both before and after the tap test. Rs-fMRI data of thirty healthy subjects were also recorded.

RESULTS

At the baseline, reduced z-DMN and z-MN scores were recorded in the iNPH cohort compared with controls. Higher z-scores were recorded in more impaired patients. Both z-scores significantly improved after the tap test except in subjects with a low resistance to outflow value and without a significant clinical improvement after the test. A statistically significant difference in mean MN connectivity scores for tap test responders and non-responders was demonstrated both before (p = 0.0236) and after the test (p = 0.00137). A statistically significant main effect of the tap test on DMN connectivity after CSF subtraction was recorded (p = 0.038).

CONCLUSIONS

Our results suggest the presence of a partially reversible plasticity functional mechanism in DMN and MN. Low values compensate for the initial stages of the disease, while higher values of z-DMN were recorded in older patients with a longer duration of symptoms, suggesting an exhausted plasticity compensation. The standardization of this technique could play a role as a non-invasive biomarker in iNPH disease, suggesting the right time for surgery. Trial Registration Prot. IRB 090/2021.

Impact of multidomain preventive strategies on functional brain connectivity in older adults with cognitive complaint: Subset from the Montpellier center of the ancillary MAPT-MRI study

Introduction

The impact of multi-domain preventive interventions on older adults, in particular on those with higher risk to develop Alzheimer's disease (AD), could be beneficial, as it may delay cognitive decline. However, the precise mechanism of such positive impact is not fully understood and may involve brain reserve and adaptability of brain functional connectivity (FC).

Methods

To determine the effect of multidomain interventions (involving physical activity, cognitive training, nutritional counseling alone or in combination with omega-3 fatty acid supplementation and vs. a placebo) on the brain, longitudinal FC changes were assessed after 36 months of intervention on 100 older adults (above 70 year-old) with subjective cognitive complaints.

Results

No global change in FC was detected after uni or multidomain preventive interventions. However, an effect of omega-3 fatty acid supplementation dependent on cognitive decline status was underlined for frontoparietal, salience, visual and sensorimotor networks FC. These findings were independent of the cortical thickness and vascular burden.

Discussion

These results emphasize the importance of patient stratification, based on risk factors, for preventive interventions.

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

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

Cerebellar gray matter alterations predict deep brain stimulation outcomes in Meige syndrome

BACKGROUND

The physiopathologic mechanism of Meige syndrome (MS) has not been clarified, and neuroimaging studies centering on cerebellar changes in MS are scarce. Moreover, even though deep brain stimulation (DBS) of the subthalamic nucleus (STN) has been recognized as an effective surgical treatment for MS, there has been no reliable biomarker to predict its efficacy.

OBJECTIVE

To characterize the volumetric alterations of gray matter (GM) in the cerebellum in MS and to identify GM measurements related to a good STN-DBS outcome.

METHODS

We used voxel-based morphometry and lobule-based morphometry to compare the regional and lobular GM differences in the cerebellum between 47 MS patients and 52 normal human controls (HCs), as well as between 31 DBS responders and 10 DBS non-responders. Both volumetric analyses were achieved using the Spatially Unbiased Infratentorial Toolbox (SUIT). Further, we performed partial correlation analyses to probe the relationship between the cerebellar GM changes and clinical scores. Finally, we plotted the receiver operating characteristic (ROC) curve to select biomarkers for MS diagnosis and DBS outcomes prediction.

RESULTS

Compared to HCs, MS patients had GM atrophy in lobule Crus I, lobule VI, lobule VIIb, lobule VIIIa, and lobule VIIIb. Compared to DBS responders, DBS non-responders had lower GM volume in the left lobule VIIIb. Moreover, partial correlation analyses revealed a positive relationship between the GM volume of the significant regions/lobules and the symptom improvement rate after DBS surgery. ROC analyses demonstrated that the GM volume of the significant cluster in the left lobule VIIIb could not only distinguish MS patients from HCs but also predict the outcomes of STN-DBS surgery with high accuracy.

CONCLUSION

MS patients display bilateral GM shrinkage in the cerebellum relative to HCs. Regional GM volume of the left lobule VIIIb can be a reliable biomarker for MS diagnosis and DBS outcomes prediction.

Influence of the Peripheral Nervous System on Murine Osteoporotic Fracture Healing and Fracture-Induced Hyperalgesia

Osteoporotic fractures are often linked to persisting chronic pain and poor healing outcomes. Substance P (SP), α-calcitonin gene-related peptide (α-CGRP) and sympathetic neurotransmitters are involved in bone remodeling after trauma and nociceptive processes, e.g., fracture-induced hyperalgesia. We aimed to link sensory and sympathetic signaling to fracture healing and fracture-induced hyperalgesia under osteoporotic conditions. Externally stabilized femoral fractures were set 28 days after OVX in wild type (WT), α-CGRP- deficient (α-CGRP -/-), SP-deficient (Tac1-/-) and sympathectomized (SYX) mice. Functional MRI (fMRI) was performed two days before and five and 21 days post fracture, followed by µCT and biomechanical tests. Sympathectomy affected structural bone properties in the fracture callus whereas loss of sensory neurotransmitters affected trabecular structures in contralateral, non-fractured bones. Biomechanical properties were mostly similar in all groups. Both nociceptive and resting-state (RS) fMRI revealed significant baseline differences in functional connectivity (FC) between WT and neurotransmitter-deficient mice. The fracture-induced hyperalgesia modulated central nociception and had robust impact on RS FC in all groups. The changes demonstrated in RS FC in fMRI might potentially be used as a bone traumata-induced biomarker regarding fracture healing under pathophysiological musculoskeletal conditions. The findings are of clinical importance and relevance as they advance our understanding of pain during osteoporotic fracture healing and provide a potential imaging biomarker for fracture-related hyperalgesia and its temporal development. Overall, this may help to reduce the development of chronic pain after fracture thereby improving the treatment of osteoporotic fractures.

Neuroimaging biomarkers of cognitive recovery after ischemic stroke

Post-stroke cognitive impairment affects more than one-third of patients after an ischemic stroke (IS). Identifying markers of potential cognitive recovery after ischemic stroke can guide patients' selection for treatments, enrollment in clinical trials, and cognitive rehabilitation methods to restore cognitive abilities in post-stroke patients. Despite the burden of post-stroke cognitive impairment, biomarkers of cognitive recovery are an understudied area of research. This narrative review summarizes and critically reviews the current literature on the use and utility of neuroimaging as a predictive biomarker of cognitive recovery after IS. Most studies included in this review utilized structural Magnetic Resonance Imaging (MRI) to predict cognitive recovery after IS; these studies highlighted baseline markers of cerebral small vessel disease and cortical atrophy as predictors of cognitive recovery. Functional Magnetic Resonance Imaging (fMRI) using resting-state functional connectivity and Diffusion Imaging are potential biomarkers of cognitive recovery after IS, although more precise predictive tools are needed. Comparison of these studies is limited by heterogeneity in cognitive assessments. For all modalities, current findings need replication in larger samples. Although no neuroimaging tool is ready for use as a biomarker at this stage, these studies suggest a clinically meaningful role for neuroimaging in predicting post-stroke cognitive recovery.

Altered functional connectivity and topology structures in default mode network induced by inflammatory exposure in aged rat: A resting-state functional magnetic resonance imaging study

Inflammatory stress in anesthesia management and surgical process has been reported to induce long-term cognitive dysfunction in vulnerable aged brain, while few studies focused on the network mechanism. The default mode network (DMN) plays a significant role in spontaneous cognitive function. Changes in topology structure and functional connectivity (FC) of DMN in vulnerable aged brain following inflammatory stress-induced long-term cognitive dysfunction are rarely studied. Eighty-eight aged male rats received intraperitoneal injection of lipopolysaccharide (LPS) as treatment or equal amount of normal saline (NS) as control. Morris Water Maze (MWM) was performed to assess short- (&lt;7 days) and long-term (&gt;30 days) learning and spatial working memory. Enzyme-linked immunosorbent assay (ELISA) was used to measure systemic and hippocampus inflammatory cytokines. Real-time polymerase chain reaction (RT-PCR) was used to measure the changes in gene level. Resting-state functional magnetic resonance imaging (rs-fMRI) was used to exam brain function prior to MWM on days 3, 7, and 31 after LPS exposure. Graph theory analysis was used to analyze FC and topology structures in aged rat DMN. Aged rats treated with LPS showed short- and long-term impairment in learning and spatial working memory in MWM test. Graph theory analysis showed temporary DMN intrinsic connectivity increased on day 3 followed with subsequent DMN intrinsic connectivity significantly altered on day 7 and day 31 in LPS-exposed rats as compared with controls. Short- and long-term alterations were observed in FC, while alterations in topology structures were only observed on day 3. Rats with inflammatory stress exposure may cause short- and long-term alterations in intrinsic connectivity in aged rat’s DMN while the changes in topology structures only lasted for 3 days. Inflammatory stress has prolonged effects on FC, but not topology structures in venerable aged brain.

Resting-state functional connectivity alteration in elderly patients with knee osteoarthritis and declined cognition: An observational study

OBJECTIVE

This study is designed to investigate the brain function changed regions in elderly patients with knee osteoarthritis (KOA) and to explore the relationship between neuropsychological tests and resting-state functional magnetic resonance imaging (rs-fMRI) network to clarify the possible mechanism underlying cognitive changes in KOA patients.

MATERIALS AND METHODS

Fifty-two patients aged ≥ 65 with KOA and twenty-two healthy-matched controls were recruited in this study. All participants were given rs-fMRI check. We used graph theory analysis to characterize functional connectivity (FC) and topological organization of the brain structural network. The relationship between FC values, topological properties, and the neuropsychological test scores was analyzed.

RESULTS

Compared with the controls, fourteen edges with lower functional connectivity were noted in the KOA group. Local efficiency and small-worldness of KOA patients decreased compared to the healthy controls. No significant alterations of nodal topological properties were found between the two groups. There was a significant positive correlation between the AVLT-H (L) and the internetwork of default mode network (DMN) (left/right orbitofrontal Superior cortex) and limbic/cortical areas (left/right caudate, right amygdala). AVLT-H(L) was positively correlated with small-worldness and local efficiency.

CONCLUSION

The results indicated that for elderly KOA patients with declined cognition, topological properties, FC between DMN and subcortical limbic network related regions are significantly decreased compared to healthy controls. These alterations demonstrated a significant correlation with the neuropsychological test scores.

Altered regional homogeneity and functional connectivity of brain activity in young HIV-infected patients with asymptomatic neurocognitive impairment

Objective

Asymptomatic neurocognitive impairment (ANI) is a predominant form of cognitive impairment in young HIV-infected patients. However, the neurophysiological mechanisms underlying this disorder have not been clarified. We aimed to evaluate the altered patterns of functional brain activity in young HIV-infected patients with ANI by quantifying regional homogeneity (ReHo) and region of interest (ROI)-based functional connectivity (FC).

Methods

The experiment involved 44 young HIV-infected patients with ANI and 47 well-matched healthy controls (HCs) undergoing resting-state functional magnetic resonance imaging (rs-fMRI) and neurocognitive tests. Reho alterations were first explored between the ANI group and HC groups. Subsequently, regions showing differences in ReHo were defined as ROIs for FC analysis. Finally, the correlation of ReHo and FC with cognitive function and clinical variables was assessed.

Results

Compared with HCs, ANI patients had a significant ReHo decrease in the right lingual gyrus (LING. R), right superior occipital gyrus (SOG. R), left superior occipital gyrus (SOG. L), left middle occipital gyrus (MOG. L), right middle frontal gyrus (MFG. R), cerebellar vermis, ReHo enhancement in the left middle frontal gyrus (MFG. L), and left insula (INS L). The ANI patients showed increased FC between the LING. R and MOG. L compared to HC. For ANI patients, verbal and language scores were negatively correlated with increased mean ReHo values in the MFG.L. Increased mean ReHo values in the INS. L was positively correlated with disease duration—the mean ReHo values in the LING. R was positively correlated with the abstraction and executive function scores. Increased FC between the LING. R and MOG. L was positively correlated with verbal and language performance.

Conclusion

The results suggest that the visual network might be the most vulnerable area of brain function in young HIV-infected patients with ANI. The middle frontal gyrus, cerebellar vermis, and insula also play an important role in asymptomatic neurocognitive impairment. The regional homogeneity and functional connectivity of these regions have compound alterations, which may be related to the course of the disease and neurocognitive function. These neuroimaging findings will help us understand the characteristics of brain network modifications in young HIV-infected patients with ANI.

A novel rhesus macaque model of Huntington's disease recapitulates key neuropathological changes along with motor and cognitive decline

We created a new nonhuman primate model of the genetic neurodegenerative disorder Huntington's disease (HD) by injecting a mixture of recombinant adeno-associated viral vectors, serotypes AAV2 and AAV2.retro, each expressing a fragment of human mutant HTT (mHTT) into the caudate and putamen of adult rhesus macaques. This modeling strategy results in expression of mutant huntingtin protein (mHTT) and aggregate formation in the injected brain regions, as well as dozens of other cortical and subcortical brain regions affected in human HD patients. We queried the disruption of cortico-basal ganglia circuitry for 30 months post-surgery using a variety of behavioral and imaging readouts. Compared to controls, mHTT-treated macaques developed working memory decline and progressive motor impairment. Multimodal imaging revealed circuit-wide white and gray matter degenerative processes in several key brain regions affected in HD. Taken together, we have developed a novel macaque model of HD that may be used to develop disease biomarkers and screen promising therapeutics.

Impaired Brain Information Transmission Efficiency and Flexibility in Parkinson’s Disease and Rapid Eye Movement Sleep Behavior Disorder: Evidence from Functional Connectivity and Functional Dynamics

Parkinson’s disease (PD) is a common neurodegenerative disorder. Rapid eye movement sleep behavior disorder (RBD) is one of the prodromal symptoms of PD. Studies have shown that brain information transmission is affected in PD patients. Consequently, we hypothesized that brain information transmission is impaired in RBD and PD. To prove our hypothesis, we performed functional connectivity (FC) and functional dynamics analysis of three aspects—based on the whole brain, within the resting-state network (RSN), and the interaction between RSNs—using normal control (NC) (n = 21), RBD (n = 24), and PD (n = 45) resting-state functional magnetic resonance imaging (rs-fMRI) data sets. Furthermore, we tested the explanatory power of FC and functional dynamics for the clinical features. Our results found that the global functional dynamics and FC of RBD and PD were impaired. Within RSN, the impairment concentrated in the visual network (VIS) and sensorimotor network (SMN), and the impaired degree of SMN in RBD was higher than that in PD. On the interaction between RSNs, RBD showed a widespread decrease, and PD showed a focal decrease which concentrated in SMN and VIS. Finally, we proved FC and functional dynamics were related to clinical features. These differences confirmed that brain information transmission efficiency and flexibility are impaired in RBD and PD, and these impairments are associated with the clinical features of patients.

Brain-age is associated with progression to dementia in memory clinic patients

BACKGROUND

Biomarkers for the early detection of dementia risk hold promise for better disease monitoring and targeted interventions. However, most biomarker studies, particularly in neuroimaging, have analysed artificially 'clean' research groups, free from comorbidities, erroneous referrals, contraindications and from a narrow sociodemographic pool. Such biases mean that neuroimaging samples are often unrepresentative of the target population for dementia risk (e.g., people referred to a memory clinic), limiting the generalisation of these studies to real-world clinical settings. To facilitate better translation from research to the clinic, datasets that are more representative of dementia patient groups are warranted.

METHODS

We analysed T1-weighted MRI scans from a real-world setting of patients referred to UK memory clinic services (n = 1140; 60.2 % female and mean [SD] age of 70.0[10.8] years) to derive 'brain-age'. Brain-age is an index of age-related brain health based on quantitative analysis of structural neuroimaging, largely reflecting brain atrophy. Brain-predicted age difference (brain-PAD) was calculated as brain-age minus chronological age. We determined which patients went on to develop dementia between three months and 7.8 years after neuroimaging assessment (n = 476) using linkage to electronic health records.

RESULTS

Survival analysis, using Cox regression, indicated a 3 % increased risk of dementia per brain-PAD year (hazard ratio [95 % CI] = 1.03 [1.02,1.04], p < 0.0001), adjusted for baseline age, age2, sex, Mini Mental State Examination (MMSE) score and normalised brain volume. In sensitivity analyses, brain-PAD remained significant when time-to-dementia was at least 3 years (hazard ratio [95 % CI] = 1.06 [1.02, 1.09], p = 0.0006), or when baseline MMSE score ≥ 27 (hazard ratio [95 % CI] = 1.03 [1.01, 1.05], p = 0.0006).

CONCLUSIONS

Memory clinic patients with older-appearing brains are more likely to receive a subsequent dementia diagnosis. Potentially, brain-age could aid decision-making during initial memory clinic assessment to improve early detection of dementia. Even when neuroimaging assessment was more than 3 years prior to diagnosis and when cognitive functioning was not clearly impaired, brain-age still proved informative. These real-world results support the use of quantitative neuroimaging biomarkers like brain-age in memory clinics.

Cholinesterase inhibitors response might be related to right hippocampal functional connectivity in mild Alzheimer's disease

BACKGROUND

The response to cholinesterase inhibitors (ChEIs) treatment is variable in patients with Alzheimer's disease (AD). Patients and physicians would benefit if these drugs could be targeted at those most likely to respond in a clinical setting. Therefore, this study aimed to evaluate the ability of CSF AD biomarkers, hippocampal volumes (HV), and Default Mode Network functional connectivity (DMN FC) to predict clinical response to ChEIs treatment in mild AD.

METHODS

We followed up on 39 mild AD patients using ChEIs at therapeutic doses. All subjects underwent clinical evaluation, neuropsychological assessment, MRI exam, and CSF biomarkers quantification at the first assessment. The Mini-Mental Status Examination (MMSE) was used to measure the global cognitive status before and after the follow-up. Were considered "Responders" those who have remained stable or improved the MMSE score between evaluations and "Non-Responders" those who have worsened the MMSE score. We have performed univariate and multivariate logistic regressions to predict the clinical response from each biomarker.

RESULTS

35.89% of patients were classified as "Responders" to ChEIs treatment after the follow-up. The multivariate model with measures of RHIPPO, adjusted for gender and interval between assessments, was significant (OR: 1.09 [CI95% 1.00 - 1.19], ρ= 0.0392). This model achieved an accuracy of 77,60%.

CONCLUSION

Our findings suggest that the functional connectivity of RHIPPO might be an early imaging biomarker to predict clinical response to ChEIs drugs in mild AD.

Modulation of cortical resting state functional connectivity during a visuospatial attention task in Parkinson's disease

Visual dysfunction is a recognized early symptom of Parkinson's disease (PD) that partly scales motor symptoms, yet its background is heterogeneous. With additional deficits in visuospatial attention, the two systems are hard to disentangle and it is not known whether impaired functional connectivity in the visual cortex is translative in nature or disrupted attentional modulation also contributes. In this study, we investigate functional connectivity modulation during a visuospatial attention task in patients with PD. In total, 15 PD and 16 age-matched healthy controls performed a visuospatial attention task while undergoing fMRI, in addition to a resting-state fMRI scan. Tensorial independent component analysis was used to investigate task-related network activity patterns. Independently, an atlas-based connectivity modulation analysis was performed using the task potency method. Spearman's rank correlation was calculated between task-related network expression, connectivity modulation, and clinical characteristics. Task-related networks including mostly visual, parietal, and prefrontal cortices were expressed to a significantly lesser degree in patients with PD (p < 0.027). Resting-state functional connectivity did not differ between the healthy and diseased cohorts. Connectivity between the precuneus and ventromedial prefrontal cortex was modulated to a higher degree in patients with PD (p < 0.004), while connections between the posterior parietal cortex and primary visual cortex, and also the superior frontal gyrus and opercular cortex were modulated to a lesser degree (p < 0.001 and p < 0.011). Task-related network expression and superior frontal gyrus–opercular cortex connectivity modulation were significantly associated with UPDRSIII motor scores and the Hoehn–Yahr stages (R = −0.72, p < 0.006 and R = −0.90, p < 0.001; R = −0.68, p < 0.01 and R = −0.71, p < 0.007). Task-related networks function differently in patients with PD in association with motor symptoms, whereas impaired modulation of visual and default-mode network connectivity was not correlated with motor function.

Subject-specific features of excitation/inhibition profiles in neurodegenerative diseases

Brain pathologies are characterized by microscopic changes in neurons and synapses that reverberate into large scale networks altering brain dynamics and functional states. An important yet unresolved issue concerns the impact of patients' excitation/inhibition profiles on neurodegenerative diseases including Alzheimer's Disease, Frontotemporal Dementia, and Amyotrophic Lateral Sclerosis. In this work, we used The Virtual Brain (TVB) simulation platform to simulate brain dynamics in healthy and neurodegenerative conditions and to extract information about the excitatory/inhibitory balance in single subjects. The brain structural and functional connectomes were extracted from 3T-MRI (Magnetic Resonance Imaging) scans and TVB nodes were represented by a Wong-Wang neural mass model endowing an explicit representation of the excitatory/inhibitory balance. Simulations were performed including both cerebral and cerebellar nodes and their structural connections to explore cerebellar impact on brain dynamics generation. The potential for clinical translation of TVB derived biophysical parameters was assessed by exploring their association with patients' cognitive performance and testing their discriminative power between clinical conditions. Our results showed that TVB biophysical parameters differed between clinical phenotypes, predicting higher global coupling and inhibition in Alzheimer's Disease and stronger N-methyl-D-aspartate (NMDA) receptor-dependent excitation in Amyotrophic Lateral Sclerosis. These physio-pathological parameters allowed us to perform an advanced analysis of patients' conditions. In backward regressions, TVB-derived parameters significantly contributed to explain the variation of neuropsychological scores and, in discriminant analysis, the combination of TVB parameters and neuropsychological scores significantly improved the discriminative power between clinical conditions. Moreover, cluster analysis provided a unique description of the excitatory/inhibitory balance in individual patients. Importantly, the integration of cerebro-cerebellar loops in simulations improved TVB predictive power, i.e., the correlation between experimental and simulated functional connectivity in all pathological conditions supporting the cerebellar role in brain function disrupted by neurodegeneration. Overall, TVB simulations reveal differences in the excitatory/inhibitory balance of individual patients that, combined with cognitive assessment, can promote the personalized diagnosis and therapy of neurodegenerative diseases.

Harmonizing functional connectivity reduces scanner effects in community detection

Community detection on graphs constructed from functional magnetic resonance imaging (fMRI) data has led to important insights into brain functional organization. Large studies of brain community structure often include images acquired on multiple scanners across different studies. Differences in scanner can introduce variability into the downstream results, and these differences are often referred to as scanner effects. Such effects have been previously shown to significantly impact common network metrics. In this study, we identify scanner effects in data-driven community detection results and related network metrics. We assess a commonly employed harmonization method and propose new methodology for harmonizing functional connectivity that leverage existing knowledge about network structure as well as patterns of covariance in the data. Finally, we demonstrate that our new methods reduce scanner effects in community structure and network metrics. Our results highlight scanner effects in studies of brain functional organization and provide additional tools to address these unwanted effects. These findings and methods can be incorporated into future functional connectivity studies, potentially preventing spurious findings and improving reliability of results.

Comorbid depressive symptoms can aggravate the functional changes of the pain matrix in patients with chronic back pain: A resting-state fMRI study

Objective

The purposes of this study are to explore (1) whether comorbid depressive symptoms in patients with chronic back pain (CBP) affect the pain matrix. And (2) whether the interaction of depression and CBP exacerbates impaired brain function.

Methods

Thirty-two patients with CBP without comorbid depressive symptoms and thirty patients with CBP with comorbid depressive symptoms were recruited. All subjects underwent functional magnetic resonance imaging (fMRI) scans. The graph theory analysis, mediation analysis, and functional connectivity (FC) analysis were included in this study. All subjects received the detection of clinical depressive symptoms and pain-related manifestations.

Result

Compared with the CBP group, subjects in the CBP with comorbid depressive symptoms (CBP-D) group had significantly increased FC in the left medial prefrontal cortex and several parietal cortical regions. The results of the graph theory analyses showed that the area under the curve of small-world property (t = −2.175, p = 0.034), gamma (t = −2.332, p = 0.023), and local efficiency (t = −2.461, p = 0.017) in the CBP-D group were significantly lower. The nodal efficiency in the ventral posterior insula (VPI) (t = −3.581, p = 0.0007), and the network efficiency values (t = −2.758, p = 0.008) in the pain matrix were significantly lower in the CBP-D group. Both the topological properties and the FC values of these brain regions were significantly correlated with self-rating depression scale (SDS) scores (all FDR corrected) but not with pain intensity. Further mediation analyses demonstrated that pain intensity had a mediating effect on the relationship between SDS scores and Pain Disability Index scores. Likewise, the SDS scores mediated the relationship between pain intensity and PDI scores.

Conclusion

Our study found that comorbid depressive symptoms can aggravate the impairment of pain matrix function of CBP, but this impairment cannot directly lead to the increase of pain intensity, which may be because some brain regions of the pain matrix are the common neural basis of depression and CBP.

Functional alterations in large-scale resting-state networks of amyotrophic lateral sclerosis: A multi-site study across Canada and the United States

Amyotrophic lateral sclerosis (ALS) is a multisystem neurodegenerative disorder characterized by progressive degeneration of upper motor neurons and lower motor neurons, and frontotemporal regions resulting in impaired bulbar, limb, and cognitive function. Magnetic resonance imaging studies have reported cortical and subcortical brain involvement in the pathophysiology of ALS. The present study investigates the functional integrity of resting-state networks (RSNs) and their importance in ALS. Intra- and inter-network resting-state functional connectivity (Rs-FC) was examined using an independent component analysis approach in a large multi-center cohort. A total of 235 subjects (120 ALS patients; 115 healthy controls (HC) were recruited across North America through the Canadian ALS Neuroimaging Consortium (CALSNIC). Intra-network and inter-network Rs-FC was evaluated by the FSL-MELODIC and FSLNets software packages. As compared to HC, ALS patients displayed higher intra-network Rs-FC in the sensorimotor, default mode, right and left fronto-parietal, and orbitofrontal RSNs, and in previously undescribed networks including auditory, dorsal attention, basal ganglia, medial temporal, ventral streams, and cerebellum which negatively correlated with disease severity. Furthermore, ALS patients displayed higher inter-network Rs-FC between the orbitofrontal and basal ganglia RSNs which negatively correlated with cognitive impairment. In summary, in ALS there is an increase in intra- and inter-network functional connectivity of RSNs underpinning both motor and cognitive impairment. Moreover, the large multi-center CALSNIC dataset permitted the exploration of RSNs in unprecedented detail, revealing previously undescribed network involvement in ALS.

Impact of Metacognitive and Psychological Factors in Learning-Induced Plasticity of Resting State Networks

Simple Summary

Connections within the brain can reshape themselves to rapidly adapt to new learning. We aimed to demonstrate that these reconfigurations do not only reflect a memory trace but a more global response to other processes involved in learning. Furthermore, we investigated why individuals do not present the same ability both in learning and in connection plasticity. Present results indicate that brain rapid reconfiguration is not only linked to learning abilities but also to the process of confidence in learning. Factors such as age, education, and anxiety also appear to influence the brain’s response to learning and explain part of the variability observed between subjects. This study revealed important links between brain and psychological functioning and how they influence each other which highlights the need for considering psychological factors both in education and in psychiatric disorders.

Abstract

While resting-state networks are able to rapidly adapt to experiences and stimuli, it is currently unknown whether metacognitive processes such as confidence in learning and psychological temperament may influence this process. We explore the neural traces of confidence in learning and their variability by: (1) targeting rs-networks in which functional connectivity (FC) modifications induced by a learning task were associated either with the participant’s performance or confidence in learning; and (2) investigating the links between FC changes and psychological temperament. Thirty healthy individuals underwent neuropsychological and psychometric evaluations as well as rs-fMRI scans before and after a visuomotor associative learning task. Confidence in learning was positively associated with the degree of FC changes in 11 connections including the cerebellar, frontal, parietal, and subcortical areas. Variability in FC changes was linked to the individual’s level of anxiety sensitivity. The present findings indicate that reconfigurations of resting state networks linked to confidence in learning differ from those linked to learning accuracy. In addition, certain temperament characteristics appear to influence these reconfigurations.

Cingulate transcranial direct current stimulation in adults with HIV

BACKGROUND

Neuronal dysfunction plays an important role in the high prevalence of HIV-associated neurocognitive disorders (HAND) in people with HIV (PWH). Transcranial direct current stimulation (tDCS)-with its capability to improve neuronal function-may have the potential to serve as an alternative therapeutic approach for HAND. Brain imaging and neurobehavioral studies provide converging evidence that injury to the anterior cingulate cortex (ACC) is highly prevalent and contributes to HAND in PWH, suggesting that ACC may serve as a potential neuromodulation target for HAND. Here we conducted a randomized, double-blind, placebo-controlled, partial crossover pilot study to test the safety, tolerability, and potential efficacy of anodal tDCS over cingulate cortex in adults with HIV, with a focus on the dorsal ACC (dACC).

METHODS

Eleven PWH (47-69 years old, 2 females, 100% African Americans, disease duration 16-36 years) participated in the study, which had two phases, Phase 1 and Phase 2. During Phase 1, participants were randomized to receive ten sessions of sham (n = 4) or cingulate tDCS (n = 7) over the course of 2-3 weeks. Treatment assignments were unknown to the participants and the technicians. Neuropsychology and MRI data were collected from four additional study visits to assess treatment effects, including one baseline visit (BL, prior to treatment) and three follow-up visits (FU1, FU2, and FU3, approximately 1 week, 3 weeks, and 3 months after treatment, respectively). Treatment assignment was unblinded after FU3. Participants in the sham group repeated the study with open-label cingulate tDCS during Phase 2. Statistical analysis was limited to data from Phase 1.

RESULTS

Compared to sham tDCS, cingulate tDCS led to a decrease in Perseverative Errors in Wisconsin Card Sorting Test (WCST), but not Non-Perseverative Errors, as well as a decrease in the ratio score of Trail Making Test-Part B (TMT-B) to TMT-Part A (TMT-A). Seed-to-voxel analysis with resting state functional MRI data revealed an increase in functional connectivity between the bilateral dACC and a cluster in the right dorsal striatum after cingulate tDCS. There were no differences in self-reported discomfort ratings between sham and cingulate tDCS.

CONCLUSIONS

Cingulate tDCS is safe and well-tolerated in PWH, and may have the potential to improve cognitive performance and brain function. A future study with a larger sample is warranted.

Classification of Parkinson's disease using a region-of-interest- and resting-state functional magnetic resonance imaging-based radiomics approach

To investigate the value of combining amplitude of low-frequency fluctuations-based radiomics and the support vector machine classifier method in distinguishing patients with Parkinson's disease from healthy controls. A total of 123 patients with Parkinson's disease and 90 healthy controls from three centers with functional and structural MRI images were included in this study. We extracted radiomics features using the Brainnetome 246 atlas from the mean amplitude of low-frequency fluctuations maps. Two-sample t-tests and recursive feature elimination combined with support vector machine method were applied for feature selection and dimensionality reduction. We used support vector machine classifier to construct model and identify the discriminative features. The automated anatomical labeling 90 atlas and fivefold cross-validation were used to evaluate the robustness and generalization of the classifier. We found our model obtained a high classification performance with an accuracy of 78.07%, and AUC, sensitivity, and specificity of 0.8597, 78.80%, and 76.08%, respectively. We detected 7 discriminative brain subregions. The fivefold cross-validation and automated anatomical labeling 90 atlas also got high classification accuracy, and we found Brainnetome 246 atlas achieved a higher classification performance than the automated anatomical labeling 90 atlas both with tenfold and fivefold cross-validation. Our findings may help the early diagnosis of Parkinson's disease and provide support for research on Parkinson's disease mechanisms and clinical evaluation.

Functional Imaging for Neurodegenerative Diseases

Diagnosis and monitoring of neurodegenerative diseases has changed profoundly over the past twenty years. Biomarkers are now included in most diagnostic procedures as well as in clinical trials. Neuroimaging biomarkers provide access to brain structure and function over the course of neurodegenerative diseases. They have brought new insights into a wide range of neurodegenerative diseases and have made it possible to describe some of the imaging challenges in clinical populations. MRI mainly explores brain structure while molecular imaging, functional MRI and electro- and magnetoencephalography examine brain function. In this paper, we describe and analyse the current and potential contribution of MRI and molecular imaging in the field of neurodegenerative diseases.

BDNF and KIBRA Polymorphisms Are Related to Altered Resting State Network Connectivity in Middle Age

BACKGROUND

Disease-modifying treatments for Alzheimer's disease (AD) may be more successful if interventions occur early, prior to significant neurodegeneration and subsequent to the onset of clinical symptoms, potentially during middle age. Polymorphisms within BDNF, COMT, and KIBRA have been implicated in AD and relate to episodic memory and executive functioning, two domains that decline early in AD.

OBJECTIVE

The purpose of the current study was to use an endophenotype approach to examine in healthy, non-demented middle-aged adults the association between polymorphisms in BDNF, COMT, and KIBRA and functional connectivity within networks related to episodic memory and executive function (i.e., default mode network (DMN), executive control network (ECN), and frontoparietal network (FPN)).

METHODS

Resting state networks were identified using independent component analysis and spatial maps with associated time courses were extracted using a dual regression approach.

RESULTS

Functional connectivity within the DMN was associated with polymorphisms in BDNF (rs11030096, rs1491850) and KIBRA (rs1030182, rs6555791, rs6555802) (ps < 0.05), ECN connectivity was associated with polymorphisms in KIBRA (rs10475878, rs6555791) (ps < 0.05), and FPN connectivity was associated with KIBRA rs6555791 (p < 0.05). There were no COMT-related differences in functional connectivity of any of the three networks investigated (ps > 0.05).

CONCLUSION

Our study demonstrates that in middle age, polymorphisms in BDNF and KIBRA are associated with altered functional connectivity in networks that are affected early in AD. Future preclinical work should consider these polymorphisms to further elucidate their role in pathological aging and to aid in the identification of biomarkers.

Structural and Functional Characterization of Gray Matter Alterations in Female Patients With Neuropsychiatric Systemic Lupus

Objective

To investigate morphological and functional alterations within gray matter (GM) in female patients with neuropsychiatric systemic lupus (NPSLE) and to explore their clinical significance.

Methods

54 female patients with SLE (30 NPSLE and 24 non-NPSLE) and 32 matched healthy controls were recruited. All subjects received a quantitative MRI scan (FLAIR, 3DT1, resting-state functional MRI). GM volume (GMV), fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), and degree of centrality (DC) were obtained. Between-group comparison, clinical correlation, and discrimination of NPSLE from non-NPSLE were achieved by voxel-based analysis, cerebellar seed-based functional connectivity analysis, regression analysis, and support vector machine (SVM), respectively.

Results

Patients with NPSLE showed overt subcortical GM atrophy without significantly abnormal brain functions in the same region compared with controls. The dysfunction within the left superior temporal gyri (L-STG) was found precede the GM volumetric loss. The function of the nodes in default mode network (DMN) and salience network (SN) were weakened in NPSLE patients compared to controls. The function of the cerebellar posterior lobes was significantly activated in non-NPSLE patients but attenuated along with GM atrophy and presented higher connectivity with L-STG and DMN in NPSLE patients, while the variation of the functional activities in the sensorimotor network (SMN) was the opposite. These structural and functional alterations were mainly correlated with disease burden and anti-phospholipid antibodies (aPLs) (r ranges from -1.53 to 1.29). The ReHos in the bilateral cerebellar posterior lobes showed high discriminative power in identifying patients with NPSLE with accuracy of 87%.

Conclusion

Patients with NPSLE exhibit both structural and functional alterations in the GM of the brain, which especially involved the deep GM, the cognitive, and sensorimotor regions, reflecting a reorganization to compensate for the disease damage to the brain which was attenuated along with pathologic burden and cerebral vascular risk factors. The GM within the left temporal lobe may be one of the direct targets of lupus-related inflammatory attack. The function of the cerebellar posterior lobes might play an essential role in compensating for cortical functional disturbances and may contribute to identifying patients with suspected NPSLE in clinical practice.