Determination of vascular dementia brain in distinct frequency bands with whole brain functional connectivity patterns

Progressive alteration of dynamic functional connectivity patterns in subcortical ischemic vascular cognitive impairment patients

Alterations in the temporal evolution of brain states in the process of cognitive impairment aggravation due to subcortical ischemic vascular disease (SIVD) is not understood. The dynamic functional connectivity was investigated to identify the abnormal temporal properties of brain states associated with cognitive impairment caused by SIVD. Eighteen patients with subcortical ischemic vascular cognitive impairment with no dementia (SIVCIND), 19 dementia patients (SIVaD) and 26 normal controls were enrolled. We found that the occupancy rate and mean lifetime of brain states were associated with cognitive performance. SIVCIND had a higher occupancy rate and longer mean lifetime in weakly connected states than normal controls. SIVaD had similar but more extensive changes in the temporal properties of brain states. In addition, switching from weakly connected states to more strongly connected states was more difficult in SIVCIND and SIVaD patients than in normal controls, especially in SIVaD patients. The results revealed that not only the transition to but also maintenance in strongly connected states became increasingly difficult when SIVD-related cognitive impairment progressed into a more severe stage.

Dissociable Functional Brain Networks Associated With Apathy in Subcortical Ischemic Vascular Disease and Alzheimer’s Disease

Few studies have investigated differences in functional connectivity (FC) between patients with subcortical ischemic vascular disease (SIVD) and Alzheimer’s disease (AD), especially in relation to apathy. Therefore, the aim of this study was to compare apathy-related FC changes among patients with SIVD, AD, and cognitively normal subjects. The SIVD group had the highest level of apathy as measured using the Apathy Evaluation Scale-clinician version (AES). Dementia staging, volume of white matter hyperintensities (WMH), and the Beck Depression Inventory were the most significant clinical predictors for apathy. Group-wise comparisons revealed that the SIVD patients had the worst level of “Initiation” by factor analysis of the AES. FCs from four resting state networks (RSNs) were compared, and the connectograms at the level of intra- and inter-RSNs revealed dissociable FC changes, shared FC in the dorsal attention network, and distinct FC in the salient network across SIVD and AD. Neuronal correlates for “Initiation” deficits that underlie apathy were explored through a regional-specific approach, which showed that the right inferior frontal gyrus, left middle frontal gyrus, and left anterior insula were the critical hubs. These findings broaden the disconnection theory by considering the effect of FC interactions across multiple RSNs on apathy formation.

Diagnostic Efficacy of Voxel-Mirrored Homotopic Connectivity in Vascular Dementia as Compared to Alzheimer's Related Neurodegenerative Diseases-A Resting State fMRI Study

Previous studies have demonstrated that functional connectivity (FC) of different brain regions in resting state function MRI were abnormal in patients suffering from mild cognitive impairment (MCI) and Alzheimer’s disease (AD) when comparing to healthy controls (HC) using seed based, independent component analysis (ICA) or small world network techniques. A new technique called voxel-mirrored homotopic connectivity (VMHC) was used in the current study to evaluate the value of interhemispheric functional connectivity (IFC) as a diagnostic tool to differentiate vascular dementia (VD) from other Alzheimer’s related neurodegenerative diseases. Eighty-three participants were recruited from the university hospital memory clinic. A multidisciplinary panel formed by a neuroradiologist and two geriatricians classified the participants into VD (13), AD (16), MCI (29), and HC (25) based on clinical history, Montreal Cognitive Assessment Hong Kong version (HK-MoCA) neuropsychological score, structural MRI, MR perfusion, and 18-F Flutametamol (amyloid) PET-CT findings of individual subjects. We adopted the calculation method used by Kelly et al. (2011) and Zuo et al. (2010) in obtaining VMHC maps. Specific patterns of VMHC maps were obtained for VD, AD, and MCI to HC comparison. VD showed significant reduction in VMHC in frontal orbital gyrus and gyrus rectus. Increased VMHC was observed in default mode network (DMN), executive control network (ECN), and the remaining salient network (SN) regions. AD showed a reduction of IFC in all DMN, ECN, and SN regions; whereas MCI showed VMHC reduction in vSN, and increased VMHC in DMN and ECN. When combining VMHC values of relevant brain regions, the accuracy was improved to 87%, 92%, and 83% for VD, AD, and MCI from HC, respectively, in receiver operating characteristic (ROC) analysis. Through studying the VMHC maps and using VMHC values in relevant brain regions, VMHC can be considered as a reliable diagnostic tool for VD, AD, and MCI from HC.

Functional brain network reorganization in HIV infection

BACKGROUND AND PURPOSE

To investigate the reorganization of the central nervous system provided by resting state-functional MRI (rs-fMRI), graph-theoretical analysis, and a newly developed functional brain network disruption index in patients with human immunodeficiency virus (HIV) infection.

METHODS

Forty HIV-positive patients without neurological impairment and 20 age- and sex-matched healthy controls underwent rs-fMRI at 3T; blood sampling was obtained the same day to evaluate biochemical variables (absolute, relative, and nadir CD4 T-lymphocytes value and plasmatic HIV-RNA). From fMRI data, disruption indices, as well as global and local graph theoretical measures, were estimated and examined for group differences (HIV vs. controls) as well as for associations with biochemical variables (HIV only). Finally, all data (global and local graph-theoretical measures, disruption indices, and biochemical variables) were tested for putative differences across three patient groups based on the duration of combined antiretroviral therapy (cART).

RESULTS

Brain function of HIV patients appeared to be deeply reorganized as compared to normal controls. The disruption index showed significant negative association with relative CD4 values, and a positive significant association between plasmatic HIV-RNA and local graph-theoretical metrics in the left lingual gyrus and the right lobule IV and V of right cerebellar hemisphere was also observed. Finally, a differential distribution of HIV clinical biomarkers and several brain metrics was observed across cART duration groups.

CONCLUSION

Our study demonstrates that rs-fMRI combined with advanced graph theoretical analysis and disruption indices is able to detect early and subtle functional changes of brain networks in HIV patients.

Amyloid and cerebrovascular burden divergently influence brain functional network changes over time

Objective

To examine the effects of baseline Alzheimer disease and cerebrovascular disease markers on longitudinal default mode network (DMN) and executive control network (ECN) functional connectivity (FC) changes in mild cognitive impairment (MCI).

Methods

We studied 30 patients with amnestic MCI (aMCI) and 55 patients with subcortical vascular MCI (svMCI) with baseline Pittsburgh Compound B (PiB)–PET scans and longitudinal MRI scans. Participants were followed up clinically with annual MRI for up to 4 years (aMCI: 26 with 2 timepoints, 4 with 3 timepoints; svMCI: 13 with 2 timepoints, 16 with 3 timepoints, 26 with 4 timepoints).

Results

β-Amyloid (Aβ) burden was associated with longitudinal DMN FC declines, while cerebrovascular burden was associated with longitudinal ECN FC changes. When patients were divided into PiB+ and PiB− groups, PiB+ patients showed longitudinal DMN FC declines, while patients with svMCI showed longitudinal ECN FC increases. Direct comparisons between the 2 groups without mixed pathology (aMCI PiB+ and svMCI PiB−) recapitulated this divergent pattern: aMCI PiB+ patients showed steeper longitudinal DMN FC declines, while svMCI PiB− patients showed steeper longitudinal ECN FC increases. Finally, using baseline PiB uptake and lacune numbers as continuous variables, baseline PiB uptake showed inverse U-shape associations with longitudinal DMN FC changes in both MCI subtypes, while baseline lacune numbers showed mainly inverse U-shape relationships with longitudinal ECN FC changes in patients with svMCI.

Conclusions

Our findings underscore the divergent effects of Aβ and cerebrovascular burden on longitudinal FC changes in the DMN and ECN in the predementia stage, which reflect the underlying pathology and may be used to track early changes in Alzheimer disease and cerebrovascular disease.

Altered static and dynamic functional network connectivity in Alzheimer's disease and subcortical ischemic vascular disease: shared and specific brain connectivity abnormalities

Subcortical ischemic vascular disease (SIVD) is a major subtype of vascular dementia with features that overlap clinically with Alzheimer's disease (AD), confounding diagnosis. Neuroimaging is a more specific and biologically based approach for detecting brain changes and thus may help to distinguish these diseases. There is still a lack of knowledge regarding the shared and specific functional brain abnormalities, especially functional connectivity changes in relation to AD and SIVD. In this study, we investigated both static functional network connectivity (sFNC) and dynamic FNC (dFNC) between 54 intrinsic connectivity networks in 19 AD patients, 19 SIVD patients, and 38 age-matched healthy controls. The results show that both patient groups have increased sFNC between the visual and cerebellar (CB) domains but decreased sFNC between the cognitive-control and CB domains. SIVD has specifically decreased sFNC within the sensorimotor domain while AD has specifically altered sFNC between the default-mode and CB domains. In addition, SIVD has more occurrences and a longer dwell time in the weakly connected dFNC states, but with fewer occurrences and a shorter dwell time in the strongly connected dFNC states. AD has both similar and opposite changes in certain dynamic features. More importantly, the dynamic features are found to be associated with cognitive performance. Our findings highlight similar and distinct functional connectivity alterations in AD and SIVD from both static and dynamic perspectives and indicate dFNC to be a more important biomarker for dementia since its progressively altered patterns can better track cognitive impairment in AD and SIVD.

Identification of Subclinical Language Deficit Using Machine Learning Classification Based on Poststroke Functional Connectivity Derived from Low Frequency Oscillations

Post-stroke neuropsychological evaluation is time-intensive in assessing impairments in subjects without overt clinical deficits. We utilized functional connectivity (FC) from ten-minute non-invasive resting-state functional MRI (rs-fMRI) to identify stroke subjects at risk for subclinical language deficit (SLD) using machine learning. Discriminative ability of FC derived from slow-5 (0.01-0.027 Hz), slow-4 (0.027-0.073 Hz) and low frequency oscillations (LFO; 0.01-0.1 Hz) was compared. Sixty clinically non-aphasic right-handed subjects were categorized into three subgroups based on stroke status and normalized verbal fluency (NVF) score: 20 ischemic early-stage stroke subjects at higher risk for SLD (LD+; mean VFS=-1.77), 20 ischemic early-stage stroke subjects with at risk for SLD (LD-; mean VFS=-0.05), 20 healthy controls (HC; mean VFS=0.29). T1-weighted and rs-fMRI were acquired within 30 days of stroke onset. Blood-oxygen-level-dependent signal was extracted within the language network. FC was evaluated and used by a multiclass support vector machine to classify test subject into a subgroup which was assessed by nested leave-one-out cross-validation. FC derived from slow-4 (70%) provided the best accuracy relative to LFO (65%) and slow-5 (50%), reasonably higher than random chance (33.33%). Using subgroup-specific accuracy, classification was best realized within slow-4 for LD+ (81.6%) and LD- (78.3%) and slow-4/LFO for HC (80%), i.e., early-stage stroke subjects showed a slow-4 FC dominance whereas HC also indicated the normalized involvement within LFO. While frontal FC differentiated stroke from healthy, occipital FC differentiated between the two stroke subgroups. Thus, stroke subjects at risk for SLD can be identified using rs-fMRI reasonably in an expedited manner.

Predicting Long-Term Cognitive Outcome Following Breast Cancer with Pre-Treatment Resting State fMRI and Random Forest Machine Learning

We aimed to determine if resting state functional magnetic resonance imaging (fMRI) acquired at pre-treatment baseline could accurately predict breast cancer-related cognitive impairment at long-term follow-up. We evaluated 31 patients with breast cancer (age 34–65) prior to any treatment, post-chemotherapy and 1 year later. Cognitive testing scores were normalized based on data obtained from 43 healthy female controls and then used to categorize patients as impaired or not based on longitudinal changes. We measured clustering coefficient, a measure of local connectivity, by applying graph theory to baseline resting state fMRI and entered these metrics along with relevant patient-related and medical variables into random forest classification. Incidence of cognitive impairment at 1 year follow-up was 55% and was predicted by classification algorithms with up to 100% accuracy (p < 0.0001). The neuroimaging-based model was significantly more accurate than a model involving patient-related and medical variables (p = 0.005). Hub regions belonging to several distinct functional networks were the most important predictors of cognitive outcome. Characteristics of these hubs indicated potential spread of brain injury from default mode to other networks over time. These findings suggest that resting state fMRI is a promising tool for predicting future cognitive impairment associated with breast cancer. This information could inform treatment decision making by identifying patients at highest risk for long-term cognitive impairment.

Influence of cerebrovascular disease on brain networks in prodromal and clinical Alzheimer's disease

Network-sensitive neuroimaging methods have been used to characterize large-scale brain network degeneration in Alzheimer’s disease and its prodrome. However, few studies have investigated the combined effect of Alzheimer’s disease and cerebrovascular disease on brain network degeneration. Our study sought to examine the intrinsic functional connectivity and structural covariance network changes in 235 prodromal and clinical Alzheimer’s disease patients with and without cerebrovascular disease. We focused particularly on two higher-order cognitive networks—the default mode network and the executive control network. We found divergent functional connectivity and structural covariance patterns in Alzheimer’s disease patients with and without cerebrovascular disease. Alzheimer’s disease patients without cerebrovascular disease, but not Alzheimer’s disease patients with cerebrovascular disease, showed reductions in posterior default mode network functional connectivity. By comparison, while both groups exhibited parietal reductions in executive control network functional connectivity, only Alzheimer’s disease patients with cerebrovascular disease showed increases in frontal executive control network connectivity. Importantly, these distinct executive control network changes were recapitulated in prodromal Alzheimer’s disease patients with and without cerebrovascular disease. Across Alzheimer’s disease patients with and without cerebrovascular disease, higher default mode network functional connectivity z-scores correlated with greater hippocampal volumes while higher executive control network functional connectivity z-scores correlated with greater white matter changes. In parallel, only Alzheimer’s disease patients without cerebrovascular disease showed increased default mode network structural covariance, while only Alzheimer’s disease patients with cerebrovascular disease showed increased executive control network structural covariance compared to controls. Our findings demonstrate the differential neural network structural and functional changes in Alzheimer’s disease with and without cerebrovascular disease, suggesting that the underlying pathology of Alzheimer’s disease patients with cerebrovascular disease might differ from those without cerebrovascular disease and reflect a combination of more severe cerebrovascular disease and less severe Alzheimer’s disease network degeneration phenotype.

Distinctive Resting State Network Disruptions Among Alzheimer's Disease, Subcortical Vascular Dementia, and Mixed Dementia Patients

BACKGROUND

Recent advances in resting-state functional MRI have revealed altered functional networks in Alzheimer's disease (AD), especially those of the default mode network (DMN) and central executive network (CEN). However, few studies have evaluated whether small vessel disease (SVD) or combined amyloid and SVD burdens affect the DMN or CEN.

OBJECTIVE

The aim of this study was to evaluate whether SVD or combined amyloid and SVD burdens affect the DMN or CEN.

METHODS

In this cross-sectional study, we investigated the resting-state functional connectivity within DMN and CEN in 37 Pittsburgh compound-B (PiB)(+) AD, 37 PiB(-) subcortical vascular dementia (SVaD), 13 mixed dementia patients, and 65 normal controls.

RESULTS

When the resting-state DMN of PiB(+) AD and PiB(-) SVaD patients were compared, the PiB(+) AD patients displayed lower functional connectivity in the inferior parietal lobule while the PiB(-) SVaD patients displayed lower functional connectivity in the medial frontal and superior frontal gyri. Compared to the PiB(-) SVaD or PiB(+) AD, the mixed dementia patients displayed lower functional connectivity within the DMN in the posterior cingulate gyrus. When the resting-state CEN connectivity of PiB(+) AD and PiB(-) SVaD patients were compared, the PiB(-) SVaD patients displayed lower functional connectivity in the anterior insular region. Compared to the PiB(-) SVaD or PiB(+) AD, the mixed dementia patients displayed lower functional connectivity within the CEN in the inferior frontal gyrus.

CONCLUSIONS

Our findings suggest that in PiB(+) AD and PiB(-) SVaD, there is divergent disruptions in resting-state DMN and CEN. Furthermore, patients with combined amyloid and SVD burdens exhibited more disrupted resting-state DMN and CEN than patients with only amyloid or SVD burden.

Abnormal intrinsic brain activity patterns in leukoaraiosis with and without cognitive impairment

The amplitude of low frequency fluctuations (ALFF) from resting-state functional MRI (rs-fMRI) signals can be used to detect intrinsic spontaneous brain activity and provide valuable insights into the pathomechanism of neural disease. In this study, we recruited 56 patients who had been diagnosed as having mild to severe leukoaraiosis. According to the neuropsychological tests, they were subdivided into a leukoaraiosis with cognitive impairment group (n = 28) and a leukoaraiosis without cognitive impairment group (n = 28). 28 volunteers were included as normal controls. We found that the three groups showed significant differences in ALFF in the brain regions of the right inferior occipital gyrus (IOG_R), left middle temporal gyrus (MTG_L), left precuneus (Pcu_L), right superior frontal gyrus (SFG_R) and right superior occipital gyrus (SOG_R). Compared with normal controls, the leukoaraiosis without cognitive impairment group exhibited significantly increased ALFF in the IOG_R, Pcu_L, SFG_R and SOG_R. While compared with leukoaraiosis without cognitive impairment group, the leukoaraiosis with cognitive impairment group showed significantly decreased ALFF in IOG_R, MTG_L, Pcu_L and SOG_R. A close negative correlation was found between the ALFF values of the MTG_L and the Montreal Cognitive Assessment (MoCA) scores. Our data demonstrate that white matter integrity and cognitive impairment are associated with different amplitude fluctuations of rs-fMRI signals. Leukoaraiosis is related to ALFF increases in IOG_R, Pcu_L, SFG_Orb_R and SOG_R. Decreased ALFF in MTG_L is characteristic of cognitive impairment and may aid in its early detection.

Characterizing Thalamocortical Disturbances in Cervical Spondylotic Myelopathy: Revealed by Functional Connectivity under Two Slow Frequency Bands

Background and Purpose

Recent advanced MRI studies on cervical spondylotic myelopathy (CSM) revealed alterations of sensorimotor cortex, but the disturbances of large-scale thalamocortical systems remains elusive. The purpose of this study was to characterizing the CSM-related thalamocortical disturbances, which were associated with spinal cord structural injury, and clinical measures.

Methods

A total of 17 patients with degenerative CSM and well-matched control subjects participated. Thalamocortical disturbances were quantified using thalamus seed-based functional connectivity in two distinct low frequencies bands (slow-5 and slow-4), with different neural manifestations. The clinical measures were evaluated by Japanese Orthopaedic Association (JOA) score system and Neck Disability Index (NDI) questionnaires.

Results

Decreased functional connectivity was found in the thalamo-motor, -somatosensory, and -temporal circuits in the slow-5 band, indicating impairment of thalamo-cortical circuit degeneration or axon/synaptic impairment. By contrast, increased functional connectivity between thalami and the bilateral primary motor (M1), primary and secondary somatosensory (S1/S2), premotor cortex (PMC), and right temporal cortex was detected in the slow-4 band, and were associated with higher fractional anisotropy values in the cervical cord, corresponding to mild spinal cord structural injury.

Conclusions

These thalamocortical disturbances revealed by two slow frequency bands inform basic understanding and vital clues about the sensorimotor dysfunction in CSM. Further work is needed to evaluate its contribution in central functional reorganization during spinal cord degeneration.

Directionality of large-scale resting-state brain networks during eyes open and eyes closed conditions

The present study examined directional connections in the brain among resting-state networks (RSNs) when the participant had their eyes open (EO) or had their eyes closed (EC). The resting-state fMRI data were collected from 20 healthy participants (9 males, 20.17 ± 2.74 years) under the EO and EC states. Independent component analysis (ICA) was applied to identify the separated RSNs (i.e., the primary/high-level visual, primary sensory-motor, ventral motor, salience/dorsal attention, and anterior/posterior default-mode networks), and the Gaussian Bayesian network (BN) learning approach was then used to explore the conditional dependencies among these RSNs. The network-to-network directional connections related to EO and EC were depicted, and a support vector machine (SVM) was further employed to identify the directional connection patterns that could effectively discriminate between the two states. The results indicated that the connections among RSNs are directionally connected within a BN during the EO and EC states. The directional connections from the salience network (SN) to the anterior/posterior default-mode networks and the high-level to primary-level visual network were the obvious characteristics of both the EO and EC resting-state BNs. Of the directional connections in BN, the directional connections of the salience and dorsal attention network (DAN) were observed to be discriminative between the EO and EC states. In particular, we noted that the properties of the salience and DANs were in opposite directions. Overall, the present study described the directional connections of RSNs using a BN learning approach during the EO and EC states, and the results suggested that the directionality of the attention systems (i.e., mainly for the salience and the DAN) in resting state might have important roles in switching between the EO and EC conditions.

Brain spontaneous fluctuations in sensorimotor regions were directly related to eyes open and eyes closed: evidences from a machine learning approach

Previous studies have demonstrated that the difference between resting-state brain activations depends on whether the subject was eyes open (EO) or eyes closed (EC). However, whether the spontaneous fluctuations are directly related to these two different resting states are still largely unclear. In the present study, we acquired resting-state functional magnetic resonance imaging data from 24 healthy subjects (11 males, 20.17 ± 2.74 years) under the EO and EC states. The amplitude of the spontaneous brain activity in low-frequency band was subsequently investigated by using the metric of fractional amplitude of low frequency fluctuation (fALFF) for each subject under each state. A support vector machine (SVM) analysis was then applied to evaluate whether the category of resting states could be determined from the brain spontaneous fluctuations. We demonstrated that these two resting states could be decoded from the identified pattern of brain spontaneous fluctuations, predominantly based on fALFF in the sensorimotor module. Specifically, we observed prominent relationships between increased fALFF for EC and decreased fALFF for EO in sensorimotor regions. Overall, the present results indicate that a SVM performs well in the discrimination between the brain spontaneous fluctuations of distinct resting states and provide new insight into the neural substrate of the resting states during EC and EO.

Different neural manifestations of two slow frequency bands in resting functional magnetic resonance imaging: a systemic survey at regional, interregional, and network levels

Temporal and spectral perspectives are two fundamental facets in deciphering fluctuating signals. In resting state, the dynamics of blood oxygen level-dependent (BOLD) signals recorded by functional magnetic resonance imaging (fMRI) have been proven to be strikingly informative (0.01-0.1 Hz). The distinction between slow-4 (0.027-0.073 Hz) and slow-5 (0.01-0.027 Hz) has been described, but the pertinent data have never been systematically investigated. This study used fMRI to measure spontaneous brain activity and to explore the different spectral characteristics of slow-4 and slow-5 at regional, interregional, and network levels, respectively assessed by regional homogeneity (ReHo) and mean amplitude of low-frequency fluctuation (mALFF), functional connectivity (FC) patterns, and graph theory. Results of paired t-tests supported/replicated recent research dividing low-frequency BOLD fluctuation into slow-4 and slow-5 for ReHo and mALFF. Interregional analyses showed that for brain regions reaching statistical significance, FC strengths at slow-4 were always weaker than those at slow-5. Community detection algorithm was applied to FC data and unveiled two modules sensitive to frequency effects: one comprised sensorimotor structure, and the other encompassed limbic/paralimbic system. Graph theoretical analysis verified that slow-4 and slow-5 differed in local segregation measures. Although the manifestation of frequency differences seemed complicated, the associated brain regions can be grossly categorized into limbic/paralimbic, midline, and sensorimotor systems. Our results suggest that future resting fMRI research addressing the three above systems either from neuropsychiatric or psychological perspectives may consider using spectrum-specific analytical strategies.

Multivariate classification of blood oxygen level-dependent FMRI data with diagnostic intention: a clinical perspective

SUMMARY

There has been a recent upsurge of reports about applications of pattern-recognition techniques from the field of machine learning to functional MR imaging data as a diagnostic tool for systemic brain disease or psychiatric disorders. Entities studied include depression, schizophrenia, attention deficit hyperactivity disorder, and neurodegenerative disorders like Alzheimer dementia. We review these recent studies which-despite the optimism from some articles-predominantly constitute explorative efforts at the proof-of-concept level. There is some evidence that, in particular, support vector machines seem to be promising. However, the field is still far from real clinical application, and much work has to be done regarding data preprocessing, model optimization, and validation. Reporting standards are proposed to facilitate future meta-analyses or systematic reviews.