Age-Related Differences in Functional Nodes of the Brain Cortex - A High Model Order Group ICA Study

COVID-19 and the Brain: A Psychological and Resting-state Functional Magnetic Resonance Imagin (fMRI) Study of the Whole-brain Functional Connectivity

Introduction

Coronavirus-2019 (COVID-19) spreads rapidly worldwide and causes severe acute respiratory syndrome. The current study aims to evaluate the relationship between the whole-brain functional connections in a resting state and cognitive impairments in patients with COVID-19 compared to the healthy control group.

Methods

Resting-state functional magnetic resonance imaging (rs-fMRI) and Montreal cognitive assessment (MoCA) data were obtained from 29 patients of the acute stage of COVID-19 on the third day of admission and 20 healthy controls. Cross-correlation of the mean resting-state signals was determined in the voxels of 23 independent components (IC) of brain neural circuits. To assess cognitive function and neuropsychological status, MoCA was performed on all participants. The relationship between rs-fMRI information, neuropsychological status, and paraclinical data was analyzed.

Results

The COVID-19 group got a lower mean MoCA score and showed a significant reduction in the functional connectivity of the IC14 (P<0.001) and IC38 (P<0.001) regions compared to the controls. The increase in functional connectivity was observed in the COVID-19 group compared to the controls at baseline in the default mode network (DMN) IC00 (P<0.001) and dorsal attention network (DAN) IC08 (P<0.001) regions. Furthermore, the alternation of functional connectivity in the mentioned ICs was significantly correlated with the mean MoCA scores and inflammatory parameters, i.e. erythrocyte sedimentation rate (ESR), and C-reactive protein (CRP).

Conclusion

Functional connectivity abnormalities in four brain neural circuits are associated with cognitive impairment and increased inflammatory markers in patients with COVID-19.

Highlights

The patients with coronavirus-2019 (COVID-19) got a lower mean Montreal cognitive assessment (MoCA) score.The patients with COVID-19 showed significant reduction in the functional connectivity of the IC14 and IC38 regions.The patients with COVID-19 showed significant increase of functional connectivity in the default mode network (DMN) IC00 and dorsal attention network (DAN) IC08 regions.Alternation of functional connectivity was significantly correlated with the mean MoCA scores and ESR and CRP.

Plain Language Summary

The researcher aimed at assessing cognitive impairments and investigating the whole-brain functional connectivity using resting state fMRI in patients with COVID-19 compared with healthy control group. The result showed That COVID-19 group got a lower mean cognitive score and showed a significant reduction in the functional connectivity of the IC14 and IC38 regions of brain compared with controls. Also, the increase of functional connectivity was observed in the COVID-19 group compared with controls at baseline in the default mode network (DMN) and dorsal attention network (DAN) regions of brain. Moreover, Functional connectivity abnormalities in four brain neural circuits associated with cognitive impairment and increased inflammatory markers in patients with COVID-19.

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.

IABC: A Toolbox for Intelligent Analysis of Brain Connectivity

Brain functional networks and connectivity have played an important role in exploring brain function for understanding the brain and disclosing the mechanisms of brain disorders. Independent component analysis (ICA) is one of the most widely applied data-driven methods to extract brain functional networks/connectivity. However, it is hard to guarantee the reliability of networks/connectivity due to the randomness of component order and the difficulty in selecting an optimal component number in ICA. To facilitate the analysis of brain functional networks and connectivity using ICA, we developed a MATLAB toolbox called Intelligent Analysis of Brain Connectivity (IABC). IABC incorporates our previously proposed group information guided independent component analysis (GIG-ICA), NeuroMark, and splitting-merging assisted reliable ICA (SMART ICA) methods, which can estimate reliable individual-subject neuroimaging measures for further analysis. After user inputs functional magnetic resonance imaging (fMRI) data of multiple subjects that are regularly organized (e.g., in Brain Imaging Data Structure (BIDS)) and clicks a few buttons to set parameters, IABC automatically outputs brain functional networks, their related time courses, and functional network connectivity of each subject. All these neuroimaging measures are promising for providing clues in understanding brain function and differentiating brain disorders.

Inter-individual differences in baseline dynamic functional connectivity are linked to cognitive aftereffects of tDCS

Transcranial direct current stimulation (tDCS) has the potential to modulate cognitive training in healthy aging; however, results from various studies have been inconsistent. We hypothesized that inter-individual differences in baseline brain state may contribute to the varied results. We aimed to explore whether baseline resting-state dynamic functional connectivity (rs-dFC) and/or conventional resting-state static functional connectivity (rs-sFC) may be related to the magnitude of cognitive aftereffects of tDCS. To achieve this aim, we used data from our double-blind randomized sham-controlled cross-over tDCS trial in 25 healthy seniors in which bifrontal tDCS combined with cognitive training had induced significant behavioral aftereffects. We performed a backward regression analysis including rs-sFC/rs-dFC measures to explain the variability in the magnitude of tDCS-induced improvements in visual object-matching task (VOMT) accuracy. Rs-dFC analysis revealed four rs-dFC states. The occurrence rate of a rs-dFC state 4, characterized by a high correlation between the left fronto-parietal control network and the language network, was significantly associated with tDCS-induced VOMT accuracy changes. The rs-sFC measure was not significantly associated with the cognitive outcome. We show that flexibility of the brain state representing readiness for top-down control of object identification implicated in the studied task is linked to the tDCS-enhanced task accuracy.

Characterizing resting-state networks in Parkinson's disease: A multi-aspect functional connectivity study

PURPOSE

Resting-state functional magnetic resonance imaging (Rs-fMRI) can be used to investigate the alteration of resting-state brain networks (RSNs) in patients with Parkinson's disease (PD) when compared with healthy controls (HCs). The aim of this study was to identify the differences between individual RSNs and reveal the most important discriminatory characteristic of RSNs between the HCs and PDs.

METHODS

This study used Rs-fMRI data of 23 patients with PD and 18 HCs. Group independent component analysis (ICA) was performed, and 23 components were extracted by spatially overlapping the components with a template RSN. The extracted components were used in the following three methods to compare RSNs of PD patients and HCs: (1) a subject-specific score based on group RSNs and a dual-regression approach (namely RSN scores); (2) voxel-wise comparison of the RSNs in the PD patient and HC groups using a nonparametric permutation test; and (3) a hierarchical clustering analysis of RSNs in the PD patient and HC groups.

RESULTS

The results of RSN scores showed a significant decrease in connectivity in seven ICs in patients with PD compared with HCs, and this decrease was particularly striking on the lateral and medial posterior occipital cortices. The results of hierarchical clustering of the RSNs revealed that the cluster of the default mode network breaks down into the three other clusters in PD patients.

CONCLUSION

We found various characteristics of the alteration of the RSNs in PD patients compared with HCs. Our results suggest that different characteristics of RSNs provide insights into the biological mechanism of PD.

Loss of Parietal Memory Network Integrity in Alzheimer's Disease

A functional brain network, termed the parietal memory network (PMN), has been shown to reflect the familiarity of stimuli in both memory encoding and retrieval. The function of this network has been separated from the commonly investigated default mode network (DMN) in both resting-state fMRI and task-activations. This study examined the deficit of the PMN in Alzheimer's disease (AD) patients using resting-state fMRI and independent component analysis (ICA) and investigated its diagnostic value in identifying AD patients. The DMN was also examined as a reference network. In addition, the robustness of the findings was examined using different types of analysis methods and parameters. Our results showed that the integrity as an intrinsic connectivity network for the PMN was significantly decreased in AD and this feature showed at least equivalent predictive ability to that for the DMN. These findings were robust to varied methods and parameters. Our findings suggest that the intrinsic connectivity of the PMN is disrupted in AD and further call for considering the PMN and the DMN separately in clinical neuroimaging studies.

The retrosplenial cortex: A memory gateway between the cortical default mode network and the medial temporal lobe

The default mode network (DMN) involves interacting cortical areas, including the posterior cingulate cortex (PCC) and the retrosplenial cortex (RSC), and subcortical areas, including the medial temporal lobe (MTL). The degree of functional connectivity (FC) within the DMN, particularly between MTL and medial-parietal subsystems, relates to episodic memory (EM) processes. However, past resting-state studies investigating the link between posterior DMN-MTL FC and EM performance yielded inconsistent results, possibly reflecting heterogeneity in the degree of connectivity between MTL and specific cortical DMN regions. Animal work suggests that RSC has structural connections to both cortical DMN regions and MTL, and may thus serve as an intermediate layer that facilitates information transfer between cortical and subcortical DMNs. We studied 180 healthy old adults (aged 64-68 years), who underwent comprehensive assessment of EM, along with resting-state fMRI. We found greater FC between MTL and RSC than between MTL and the other cortical DMN regions (e.g., PCC), with the only significant association with EM observed for MTL-RSC FC. Mediational analysis showed that MTL-cortical DMN connectivity increased with RSC as a mediator. Further analysis using a graph-theoretical approach on DMN nodes revealed the highest betweenness centrality for RSC, confirming that a high proportion of short paths among DMN regions pass through RSC. Importantly, the degree of RSC mediation was associated with EM performance, suggesting that individuals with greater mediation have an EM advantage. These findings suggest that RSC forms a critical gateway between MTL and cortical DMN to support EM in older adults.

Multimodal Discrimination of Schizophrenia Using Hybrid Weighted Feature Concatenation of Brain Functional Connectivity and Anatomical Features with an Extreme Learning Machine

Multimodal features of structural and functional magnetic resonance imaging (MRI) of the human brain can assist in the diagnosis of schizophrenia. We performed a classification study on age, sex, and handedness-matched subjects. The dataset we used is publicly available from the Center for Biomedical Research Excellence (COBRE) and it consists of two groups: patients with schizophrenia and healthy controls. We performed an independent component analysis and calculated global averaged functional connectivity-based features from the resting-state functional MRI data for all the cortical and subcortical anatomical parcellation. Cortical thickness along with standard deviation, surface area, volume, curvature, white matter volume, and intensity measures from the cortical parcellation, as well as volume and intensity from sub-cortical parcellation and overall volume of cortex features were extracted from the structural MRI data. A novel hybrid weighted feature concatenation method was used to acquire maximal 99.29% (P < 0.0001) accuracy which preserves high discriminatory power through the weight of the individual feature type. The classification was performed by an extreme learning machine, and its efficiency was compared to linear and non-linear (radial basis function) support vector machines, linear discriminant analysis, and random forest bagged tree ensemble algorithms. This article reports the predictive accuracy of both unimodal and multimodal features after 10-by-10-fold nested cross-validation. A permutation test followed the classification experiment to assess the statistical significance of the classification results. It was concluded that, from a clinical perspective, this feature concatenation approach may assist the clinicians in schizophrenia diagnosis.

Dissociated patterns of anti-correlations with dorsal and ventral default-mode networks at rest

Previous studies of resting state functional connectivity have demonstrated that the default-mode network (DMN) is negatively correlated with a set of brain regions commonly activated during goal-directed tasks. However, the location and extent of anti-correlations are inconsistent across different studies, which has been posited to result largely from differences in whether or not global signal regression (GSR) was applied as a pre-processing step. Notably, coordinates of seed regions-of-interest defined within the posterior cingulate cortex (PCC)/precuneus, an area often employed to study functional connectivity of the DMN, have been inconsistent across studies. Taken together with recent observations that the DMN contains functionally heterogeneous subdivisions, it is presently unclear whether these seeds map to different DMN subnetworks, whose patterns of anti-correlation may differ. If so, then seed location may be a non-negligible factor that, in addition to differences in preprocessing steps, contributes to the inconsistencies reported among published studies regarding DMN correlations/anti-correlations. In this study, they examined anti-correlations of different subnetworks within the DMN during rest using both seed-based and point process analyses, and discovered that: (1) the ventral branch of the DMN (vDMN) yielded significantly weaker anti-correlations than that associated with the dorsal branch of the DMN (dDMN); (2) vDMN anti-correlations introduced by GSR were distinct from dDMN anti-correlations; (3) PCC/precuneus seeds employed by earlier studies mapped to different DMN subnetworks, which may explain some of the inconsistency (in addition to preprocessing steps) in the reported DMN anti-correlations. Hum Brain Mapp 38:2454-2465, 2017. © 2017 Wiley Periodicals, Inc.

Concordance of the Resting State Networks in Typically Developing, 6-to 7-Year-Old Children and Healthy Adults

Though fairly well-studied in adults, less is known about the manifestation of resting state networks (RSN) in children. We examined the validity of RSN derived in an ethnically diverse group of typically developing 6- to 7-year-old children. We hypothesized that the RSNs in young children would be robust and would reliably show significant concordance with previously published RSN in adults. Additionally, we hypothesized that a smaller sample size using this robust technique would be comparable in quality to pediatric RSNs found in a larger cohort study. Furthermore, we posited that compared to the adult RSNs, the primary sensorimotor and the default mode networks (DMNs) in this pediatric group would demonstrate the greatest correspondence, while the executive function networks would exhibit a lesser degree of spatial overlap. Resting state functional magnetic resonance images (rs-fMRI) were acquired in 18 children between 6 and 7 years recruited from an ethnically diverse population in the Mid-South region of the United States. Twenty RSNs were derived using group independent component analysis and their spatial correspondence with previously published adult RSNs was examined. We demonstrate that the rs-fMRI in this group can be deconstructed into the fundamental RSN as all the major RSNs previously described in adults and in a large sample that included older children can be observed in our sample of young children. Further, the primary visual, auditory, and somatosensory networks, as well as the default mode, and frontoparietal networks derived in this group exhibited a greater spatial concordance with those seen in adults. The motor, temporoparietal, executive control, dorsal attention, and cerebellar networks in children had less spatial overlap with the corresponding RSNs in adults. Our findings suggest that several salient RSNs can be mapped reliably in small and diverse pediatric cohort within a narrow age range and the evolution of these RSNs can be studied reliably in such groups during early childhood and adolescence.

The Effect of Gray Matter ICA and Coefficient of Variation Mapping of BOLD Data on the Detection of Functional Connectivity Changes in Alzheimer's Disease and bvFTD

Resting-state fMRI results in neurodegenerative diseases have been somewhat conflicting. This may be due to complex partial volume effects of CSF in BOLD signal in patients with brain atrophy. To encounter this problem, we used a coefficient of variation (CV) map to highlight artifacts in the data, followed by analysis of gray matter voxels in order to minimize brain volume effects between groups. The effects of these measures were compared to whole brain ICA dual regression results in Alzheimer’s disease (AD) and behavioral variant frontotemporal dementia (bvFTD). 23 AD patients, 21 bvFTD patients and 25 healthy controls were included. The quality of the data was controlled by CV mapping. For detecting functional connectivity (FC) differences whole brain ICA (wbICA) and also segmented gray matter ICA (gmICA) followed by dual regression were conducted, both of which were performed both before and after data quality control. Decreased FC was detected in posterior DMN in the AD group and in the Salience network in the bvFTD group after combining CV quality control with gmICA. Before CV quality control, the decreased connectivity finding was not detectable in gmICA in neither of the groups. Same finding recurred when exclusion was based on randomization. The subjects excluded due to artifacts noticed in the CV maps had significantly lower temporal signal-to-noise ratio than the included subjects. Data quality measure CV is an effective tool in detecting artifacts from resting state analysis. CV reflects temporal dispersion of the BOLD signal stability and may thus be most helpful for spatial ICA, which has a blind spot in spatially correlating widespread artifacts. CV mapping in conjunction with gmICA yields results suiting previous findings both in AD and bvFTD.

Aberrant neural networks for the recognition memory of socially relevant information in patients with schizophrenia

INTRODUCTION

Patients with schizophrenia exhibit several cognitive deficits, including memory impairment. Problems with recognition memory can hinder socially adaptive behavior. Previous investigations have suggested that altered activation of the frontotemporal area plays an important role in recognition memory impairment. However, the cerebral networks related to these deficits are not known. The aim of this study was to elucidate the brain networks required for recognizing socially relevant information in patients with schizophrenia performing an old-new recognition task.

METHODS

Sixteen patients with schizophrenia and 16 controls participated in this study. First, the subjects performed the theme-identification task during functional magnetic resonance imaging. In this task, pictures depicting social situations were presented with three words, and the subjects were asked to select the best theme word for each picture. The subjects then performed an old-new recognition task in which they were asked to discriminate whether the presented words were old or new. Task performance and neural responses in the old-new recognition task were compared between the subject groups. An independent component analysis of the functional connectivity was performed.

RESULTS

The patients with schizophrenia exhibited decreased discriminability and increased activation of the right superior temporal gyrus compared with the controls during correct responses. Furthermore, aberrant network activities were found in the frontopolar and language comprehension networks in the patients.

CONCLUSIONS

The functional connectivity analysis showed aberrant connectivity in the frontopolar and language comprehension networks in the patients with schizophrenia, and these aberrations possibly contribute to their low recognition performance and social dysfunction. These results suggest that the frontopolar and language comprehension networks are potential therapeutic targets in patients with schizophrenia.

Resting-state networks in 6-to-10 year old children

Resting-state functional magnetic resonance imaging provides a non-invasive approach to the study of intrinsic functional brain networks. When applied to the study of brain development, most studies consist of relatively small samples that are not always representative of the general population. Descriptions of these networks in the general population offer important insight for clinical studies examining, for instance, psychopathology or neurological conditions. Thus our goal was to characterize resting-state networks in a large sample of children using independent component analysis (ICA). The study further aimed to describe the robustness of these networks by examining which networks occur frequently after repeated ICA. Resting-state networks were obtained from a sample of 536 6-to-10 year old children. Distributions of networks were built from repeated subsampling and group ICA analyses, and meta-ICA was used to construct a representative set of components. Within- and between-network properties were tested for age-related developmental associations using spatio-temporal regression. After repeated ICA, many networks were present over 95% of the time suggesting the components are highly reproducible. Some networks were less robust, and were observed less than 70% of the time. Age-related associations were also observed in a selection of networks, including the default-mode network, offering further evidence of development in these networks at an early age. ICA-derived resting-state networks appear to be robust, although some networks should further scrutinized if subjected to group-level statistical analyses, such as spatiotemporal regression. The final set of ICA-derived networks and an age-appropriate T₁ -weighted template are made available to the neuroimaging community, https://www.nitrc.org/projects/genr. Hum Brain Mapp 37:4286-4300, 2016. © 2016 Wiley Periodicals, Inc.

ICA-based artifact removal diminishes scan site differences in multi-center resting-state fMRI

Resting-state fMRI (R-fMRI) has shown considerable promise in providing potential biomarkers for diagnosis, prognosis and drug response across a range of diseases. Incorporating R-fMRI into multi-center studies is becoming increasingly popular, imposing technical challenges on data acquisition and analysis, as fMRI data is particularly sensitive to structured noise resulting from hardware, software, and environmental differences. Here, we investigated whether a novel clean up tool for structured noise was capable of reducing center-related R-fMRI differences between healthy subjects. We analyzed three Tesla R-fMRI data from 72 subjects, half of whom were scanned with eyes closed in a Philips Achieva system in The Netherlands, and half of whom were scanned with eyes open in a Siemens Trio system in the UK. After pre-statistical processing and individual Independent Component Analysis (ICA), FMRIB's ICA-based X-noiseifier (FIX) was used to remove noise components from the data. GICA and dual regression were run and non-parametric statistics were used to compare spatial maps between groups before and after applying FIX. Large significant differences were found in all resting-state networks between study sites before using FIX, most of which were reduced to non-significant after applying FIX. The between-center difference in the medial/primary visual network, presumably reflecting a between-center difference in protocol, remained statistically significant. FIX helps facilitate multi-center R-fMRI research by diminishing structured noise from R-fMRI data. In doing so, it improves combination of existing data from different centers in new settings and comparison of rare diseases and risk genes for which adequate sample size remains a challenge.

Lateralized resting-state functional connectivity in the task-positive and task-negative networks

Studies on functional brain lateralization using functional magnetic resonance imaging (fMRI) have generally focused on lateralization of local brain regions. To explore the lateralization on the whole-brain level, lateralization of functional connectivity using resting-state fMRI (N=87, right handed) was analyzed and left- and right-lateralized networks were mapped. Four hundred two equally spaced regions of interest (ROI) covering the entire gray matter were divided into 358 task-positive and 44 task-negative ROIs. Lateralization of functional connectivity was analyzed separately for the task-positive and task-negative regions to prevent spuriously high lateralization indices caused by negative correlations between task-positive and task-negative regions. Lateralized functional connections were obtained using k-means clustering analysis. Within the task-positive network, the right-lateralized functional connections were between the occipital and inferior/middle frontal regions among other connections, whereas the left-lateralized functional connections were among fusiform gyrus and inferior frontal and inferior/superior parietal regions. Within the task-negative network, the left-lateralized connections were mainly between the precuneus and medial prefrontal regions. Specific brain regions exhibited different left- or right-lateralized connections with other regions, which suggest the importance of reporting lateralized connections over lateralized seed regions. The mean lateralization indices of the left- and right-lateralized connections were correlated, suggesting that the lateralization of connectivity may result from complementary processes between the lateralized networks. The potential functions of the lateralized networks were discussed.

Aberrant Functional Connectivity in the Default Mode and Central Executive Networks in Subjects with Schizophrenia - A Whole-Brain Resting-State ICA Study

Neurophysiological changes of schizophrenia are currently linked to disturbances in connectivity between functional brain networks. Functional magnetic resonance imaging studies on schizophrenia have focused on a few selected networks. Also previously, it has not been possible to discern whether the functional alterations in schizophrenia originate from spatial shifting or amplitude alterations of functional connectivity. In this study, we aim to discern the differences in schizophrenia patients with respect to spatial shifting vs. signal amplitude changes in functional connectivity in the whole-brain connectome. We used high model order-independent component analysis to study some 40 resting-state networks (RSN) covering the whole cortex. Group differences were analyzed with dual regression coupled with y-concat correction for multiple comparisons. We investigated the RSNs with and without variance normalization in order to discern spatial shifting from signal amplitude changes in 43 schizophrenia patients and matched controls from the Northern Finland 1966 Birth Cohort. Voxel-level correction for multiple comparisons revealed 18 RSNs with altered functional connectivity, 6 of which had both spatial and signal amplitude changes. After adding the multiple comparison, y-concat correction to the analysis for including the 40 RSNs as well, we found that four RSNs showed still changes. These robust changes actually seem encompass parcellations of the default mode network and central executive networks. These networks both have spatially shifted connectivity and abnormal signal amplitudes. Interestingly the networks seem to mix their functional representations in areas like left caudate nucleus and dorsolateral prefrontal cortex. These changes overlapped with areas that have been related to dopaminergic alterations in patients with schizophrenia compared to controls.

Altered resting-state activity in seasonal affective disorder

At present, our knowledge about seasonal affective disorder (SAD) is based mainly up on clinical symptoms, epidemiology, behavioral characteristics and light therapy. Recently developed measures of resting-state functional brain activity might provide neurobiological markers of brain disorders. Studying functional brain activity in SAD could enhance our understanding of its nature and possible treatment strategies. Functional network connectivity (measured using ICA-dual regression), and amplitude of low-frequency fluctuations (ALFF) were measured in 45 antidepressant-free patients (39.78 ± 10.64, 30 ♀, 15 ♂) diagnosed with SAD and compared with age-, gender- and ethnicity-matched healthy controls (HCs) using resting-state functional magnetic resonance imaging. After correcting for Type 1 error at high model orders (inter-RSN correction), SAD patients showed significantly increased functional connectivity in 11 of the 47 identified RSNs. Increased functional connectivity involved RSNs such as visual, sensorimotor, and attentional networks. Moreover, our results revealed that SAD patients compared with HCs showed significant higher ALFF in the visual and right sensorimotor cortex. Abnormally altered functional activity detected in SAD supports previously reported attentional and psychomotor symptoms in patients suffering from SAD. Further studies, particularly under task conditions, are needed in order to specifically investigate cognitive deficits in SAD.

GroupICA dual regression analysis of resting state networks in a behavioral variant of frontotemporal dementia

Functional MRI studies have revealed changes in default-mode and salience networks in neurodegenerative dementias, especially in Alzheimer's disease (AD). The purpose of this study was to analyze the whole brain cortex resting state networks (RSNs) in patients with behavioral variant frontotemporal dementia (bvFTD) by using resting state functional MRI (rfMRI). The group specific RSNs were identified by high model order independent component analysis (ICA) and a dual regression technique was used to detect between-group differences in the RSNs with p < 0.05 threshold corrected for multiple comparisons. A y-concatenation method was used to correct for multiple comparisons for multiple independent components, gray matter differences as well as the voxel level. We found increased connectivity in several networks within patients with bvFTD compared to the control group. The most prominent enhancement was seen in the right frontotemporal area and insula. A significant increase in functional connectivity was also detected in the left dorsal attention network (DAN), in anterior paracingulate—a default mode sub-network as well as in the anterior parts of the frontal pole. Notably the increased patterns of connectivity were seen in areas around atrophic regions. The present results demonstrate abnormal increased connectivity in several important brain networks including the DAN and default-mode network (DMN) in patients with bvFTD. These changes may be associated with decline in executive functions and attention as well as apathy, which are the major cognitive and neuropsychiatric defects in patients with frontotemporal dementia.

Spatial vs. Temporal Features in ICA of Resting-State fMRI - A Quantitative and Qualitative Investigation in the Context of Response Inhibition

Independent component analysis (ICA) can identify covarying functional networks in the resting brain. Despite its relatively widespread use, the potential of the temporal information (unlike spatial information) obtained by ICA from resting state fMRI (RS-fMRI) data is not always fully utilized. In this study, we systematically investigated which features in ICA of resting-state fMRI relate to behaviour, with stop signal reaction time (SSRT) in a stop-signal task taken as a test case. We did this by correlating SSRT with the following three kinds of measure obtained from RS-fMRI data: (1) the amplitude of each resting state network (RSN) (evaluated by the standard deviation of the RSN timeseries), (2) the temporal correlation between every pair of RSN timeseries, and (3) the spatial map of each RSN. For multiple networks, we found significant correlations not only between SSRT and spatial maps, but also between SSRT and network activity amplitude. Most of these correlations are of functional interpretability. The temporal correlations between RSN pairs were of functional significance, but these correlations did not appear to be very sensitive to finding SSRT correlations. In addition, we also investigated the effects of the decomposition dimension, spatial smoothing and Z-transformation of the spatial maps, as well as the techniques for evaluating the temporal correlation between RSN timeseries. Overall, the temporal information acquired by ICA enabled us to investigate brain function from a complementary perspective to the information provided by spatial maps.

Changes in cerebral morphometry and amplitude of low-frequency fluctuations of BOLD signals during healthy aging: correlation with inhibitory control

Aging is known to be associated with changes in cerebral morphometry and in regional activations during resting or cognitive challenges. Here, we investigated the effects of age on cerebral gray matter (GM) volumes and fractional amplitude of low-frequency fluctuation (fALFF) of blood oxygenation level-dependent signals in 111 healthy adults, 18-72 years of age. GM volumes were computed using voxel-based morphometry as implemented in Statistical Parametric Mapping, and fALFF maps were computed for task-residuals as described in Zhang and Li (Neuroimage 49:1911-1918, 2010) for individual participants. Across participants, a simple regression against age was performed for GM volumes and fALFF, respectively, with quantity of recent alcohol use as a covariate. At cluster level p < 0.05, corrected for family-wise error of multiple comparisons, GM volumes declined with age in prefrontal/frontal regions, bilateral insula, and left inferior parietal lobule (IPL), suggesting structural vulnerability of these areas to aging. FALFF was negatively correlated with age in the supplementary motor area (SMA), pre-SMA, anterior cingulate cortex, bilateral dorsal lateral prefrontal cortex (DLPFC), right IPL, and posterior cingulate cortex, indicating that spontaneous neural activities in these areas during cognitive performance decrease with age. Notably, these age-related changes overlapped in the prefrontal/frontal regions including the pre-SMA, SMA, and DLPFC. Furthermore, GM volumes and fALFF of the pre-SMA/SMA were negatively correlated with the stop signal reaction time, in accord with our earlier work. Together, these results describe anatomical and functional changes in prefrontal/frontal regions and how these changes are associated with declining inhibitory control during aging.

Functional brain connectivity at rest changes after working memory training

Networks of functional connectivity are highly consistent across participants, suggesting that functional connectivity is for a large part predetermined. However, several studies have shown that functional connectivity may change depending on instructions or previous experience. In the present study, we investigated whether 6 weeks of practice with a working memory task changes functional connectivity during a resting period preceding the task. We focused on two task-relevant networks, the frontoparietal network and the default network, using seed regions in the right middle frontal gyrus (MFG) and the medial prefrontal cortex (PFC), respectively. After practice, young adults showed increased functional connectivity between the right MFG and other regions of the frontoparietal network, including bilateral superior frontal gyrus, paracingulate gyrus, and anterior cingulate cortex. In addition, they showed reduced functional connectivity between the medial PFC and right posterior middle temporal gyrus. Moreover, a regression with performance changes revealed a positive relation between performance increases and changes of frontoparietal connectivity, and a negative relation between performance increases and changes of default network connectivity. Next, to study whether experience-dependent effects would be different during development, we also examined practice effects in a pilot sample of 12-year-old children. No practice effects were found in this group, suggesting that practice-related changes of functional connectivity are age-dependent. Nevertheless, future studies with larger samples are necessary to confirm this hypothesis.

Gender modulates the APOE ε4 effect in healthy older adults: convergent evidence from functional brain connectivity and spinal fluid tau levels

We examined whether the effect of the apolipoprotein E (APOE) genotype on functional brain connectivity is modulated by gender in healthy older human adults. Our results confirm significantly decreased connectivity in the default mode network in healthy older APOE ε4 carriers compared with ε3 homozygotes. More important, further testing revealed a significant interaction between APOE genotype and gender in the precuneus, a major default mode hub. Female ε4 carriers showed significantly reduced default mode connectivity compared with either female ε3 homozygotes or male ε4 carriers, whereas male ε4 carriers differed minimally from male ε3 homozygotes. An additional analysis in an independent sample of healthy elderly using an independent marker of Alzheimer's disease, i.e., spinal fluid levels of tau, provided corresponding evidence for this gender-by-APOE interaction. Together, these results converge with previous work showing a higher prevalence of the ε4 allele among women with Alzheimer's disease and, critically, demonstrate that this interaction between APOE genotype and gender is detectable in the preclinical period.

Dynamic functional reorganizations and relationship with working memory performance in healthy aging

In recent years, several theories have been proposed in attempts to identify the neural mechanisms underlying successful cognitive aging. Old subjects show increased neural activity during the performance of tasks, mainly in prefrontal areas, which is interpreted as a compensatory mechanism linked to functional brain efficiency. Moreover, resting-state studies have concluded that elders show disconnection or disruption of large-scale functional networks. We used functional MRI during resting-state and a verbal n-back task with different levels of memory load in a cohort of young and old healthy adults to identify patterns of networks associated with working memory and brain default mode. We found that the disruption of resting-state networks in the elderly coexists with task-related overactivations of certain brain areas and with reorganizations within these functional networks. Moreover, elders who were able to activate additional areas and to recruit a more bilateral frontal pattern within the task-related network achieved successful performance on the task. We concluded that the balanced and plastic reorganization of brain networks underlies successful cognitive aging. This observation allows the integration of several theories that have been proposed to date regarding the aging brain.

Functional connectivity tracks clinical deterioration in Alzheimer's disease

While resting state functional connectivity has been shown to decrease in patients with mild and/or moderate Alzheimer's disease, it is not yet known how functional connectivity changes in patients as the disease progresses. Furthermore, it has been noted that the default mode network is not as homogenous as previously assumed and several fractionations of the network have been proposed. Here, we separately investigated the modulation of 3 default mode subnetworks, as identified with group independent component analysis, by comparing Alzheimer's disease patients to healthy controls and by assessing connectivity changes over time. Our results showed decreased connectivity at baseline in patients versus controls in the posterior default mode network, and increased connectivity in the anterior and ventral default mode networks. At follow-up, functional connectivity decreased across all default mode systems in patients. Our results suggest that earlier in the disease, regions of the posterior default mode network start to disengage whereas regions within the anterior and ventral networks enhance their connectivity. However, as the disease progresses, connectivity within all systems eventually deteriorates.

Group-ICA Model Order Highlights Patterns of Functional Brain Connectivity

Resting-state networks (RSNs) can be reliably and reproducibly detected using independent component analysis (ICA) at both individual subject and group levels. Altering ICA dimensionality (model order) estimation can have a significant impact on the spatial characteristics of the RSNs as well as their parcellation into sub-networks. Recent evidence from several neuroimaging studies suggests that the human brain has a modular hierarchical organization which resembles the hierarchy depicted by different ICA model orders. We hypothesized that functional connectivity between-group differences measured with ICA might be affected by model order selection. We investigated differences in functional connectivity using so-called dual regression as a function of ICA model order in a group of unmedicated seasonal affective disorder (SAD) patients compared to normal healthy controls. The results showed that the detected disease-related differences in functional connectivity alter as a function of ICA model order. The volume of between-group differences altered significantly as a function of ICA model order reaching maximum at model order 70 (which seems to be an optimal point that conveys the largest between-group difference) then stabilized afterwards. Our results show that fine-grained RSNs enable better detection of detailed disease-related functional connectivity changes. However, high model orders show an increased risk of false positives that needs to be overcome. Our findings suggest that multilevel ICA exploration of functional connectivity enables optimization of sensitivity to brain disorders.