The neurocognitive correlates of brain entropy estimated by resting state fMRI

The relationships of resting-state brain entropy (BEN), ovarian hormones and behavioral inhibition and activation systems (BIS/BAS)

Brain entropy (BEN) quantifies irregularity, disorder and uncertainty of brain activity. Recent studies have linked BEN, derived from resting-state functional magnetic resonance imaging (rs-fMRI), to cognition, task activation, neuromodulation, and pharmacological interventions. However, it remains unknown whether BEN can reflect the effects of hormonal fluctuations. Furthermore, ovarian hormones are known to modulate behavioral traits, such as inhibitory control and impulsivity, as measured by the Behavioral Inhibition and Activation Systems (BIS/BAS). In this study, we investigated how ovarian hormones influence BEN and BIS/BAS in young adult women. The forty-four participants (mean age = 22.61 ± 2.14 years) were obtained from OpenNeuro in the study. Ovarian hormones including estradiol (E2), progesterone (PROG) and BIS/BAS were acquired before scanning. The voxel-wise BEN maps were calculated from the preprocessed rs-fMRI images. Pearson's correlation and mediation analyses were used to assess the relationships between BEN and ovarian hormones as well as BIS/BAS. Our results revealed a negative correlation between BEN and PROG in frontoparietal network (FPN), including the dorsolateral prefrontal cortex (DLPFC) and posterior parietal cortex (PPC), as well as in the limbic network, encompassing the amygdala, hippocampus, and parahippocampal cortex. In contrast, BEN showed a positive correlation with impulsivity traits measured by the BAS-drive subscale of BAS in the left DLPFC. Additionally, PROG was negatively correlated with impulsivity traits measured by BAS-drive. Results from mediation analysis demonstrated that PROG reduces impulsivity, as measured by BAS-drive, by decreasing BEN in the left DLPFC and subsequently increasing functional connectivity (FC) within this region. These findings provide the first evidence that BEN reflects the influence of PROG on brain function and behavior. Furthermore, they elucidate the neural mechanisms through which PROG modulates impulsivity traits measured by BAS-drive: PROG enhances the temporal coherence (decreased entropy) of neural activity in the left DLPFC, which in turn increases temporal synchronization (increased FC) within this region during resting-state, and then enhances executive control functions, thereby negatively regulating impulsivity. These findings provide new insights into our understanding of the effects of ovarian hormones on the brain and behavior in women.

Normative Brain Entropy Across the Lifespan

Brain entropy (BEN), a measure of the complexity and irregularity of neural has emerged as a promising marker for cognitive and clinical traits. However, normative lifespan trajectories of BEN remain underexplored. In this study, we investigated age-related changes in BEN across the human lifespan using Sample Entropy (SampEn). BEN was estimated from resting-state fMRI data collected from multiple Human Connectome Project cohorts (N = 2,415, ages 8-89 years), and normative growth curves were modeled using the GAMLSS framework. Results revealed a nonlinear increase in average BEN from childhood to older adulthood, with females exhibiting significantly higher BEN than males. Regional and network-level analyses confirmed similar age-related patterns.

Chronic pain is associated with greater brain entropy in the prefrontal cortex

Chronic pain is a debilitating clinical condition and a severe public health issue that demands to be addressed. Neuroimaging-based techniques have been widely adopted to investigate the neural underpinnings of chronic pain. Despite the efforts the complex nature of pain experience as well as the heterogeneity of chronic pain have made the identification of neuroimaging-based biomarkers extremely challenging. In this study, resting-state fMRI-based brain entropy, a measure reflecting the "irregularity" of brain activity, was adopted as a biomarker of chronic pain by comparing individuals with chronic pain and healthy controls in a sample of middle-to-old-age participants (n > 30,000) drawn from the UK Biobank database. Abnormal brain entropy is associated with altered brain dynamics and may serve as a potential marker of disrupted pain processing in individuals with chronic pain. Compared to healthy controls, individuals with chronic pain exhibited increased brain entropy in a broad set of regions including the frontal, temporal, and occipital lobes, as well as the cerebellum. In addition, individuals with a more distributed chronic pain showed increased brain entropy in occipital lobes. When examining distinct types of chronic pain individually, only participants with headache and pain all over the body showed brain entropy differences compared to a matched sample of healthy controls. PERSPECTIVE: This article investigates the neural substrates of chronic pain using brain entropy, a measure of the randomness and irregularity of brain activity. This measure could potentially aid in the assessment and treatment of chronic pain.

Normalization and cross-entropy connectivity in brain disease classification

In resting-state functional magnetic resonance imaging (rs-fMRI), Pearson correlation has traditionally been the dominant method for constructing brain connectivity. This paper introduces an entropy-based connectivity approach utilizing subject-level Z score normalization, which not only standardizes signal amplitudes across subjects but also preserves interregional signal differences more effectively than Pearson correlation. Furthermore, the proposed method incorporates cross-entropy techniques, offering an advanced perspective on the temporal ordering of signals between brain regions rather than merely capturing their synchronization. Experimental results demonstrate that the proposed subject-normalized cross-joint entropy achieves superior classification accuracy in schizophrenia, mild cognitive impairment, and autism spectrum disorder, outperforming the conventional normalized correlation method by approximately 4%, 6%, and 7%, respectively. Additionally, the observed performance improvement may be attributed to changes in the symmetry of functional connectivity between brain regions-an aspect often overlooked in traditional functional connectivity analyses.

The effects of intermittent theta burst stimulation (iTBS) on resting-state brain entropy (BEN)

Intermittent theta burst stimulation (iTBS), a novel protocol within repetitive transcranial magnetic stimulation (rTMS), has shown superior therapeutic effects for depression compared to conventional high-frequency rTMS (HF-rTMS). However, the neural mechanisms underlying iTBS remain poorly understood. Brain entropy (BEN), a measure of the irregularity of brain activity, has recently emerged as a promising marker for regional brain function and has demonstrated sensitivity to depression and HF-rTMS. Given its potential, BEN may help elucidate the mechanisms of iTBS. In this study, we computed BEN using resting-state fMRI data from sixteen healthy participants obtained from OpenNeuro. Participants underwent iTBS over the left dorsolateral prefrontal cortex (L-DLPFC) at two different intensities (90 ​% and 120 ​% of resting motor threshold (rMT)) on separate days. We used a 2 ​× ​2 repeated measures analysis of variance (ANOVA) to analyze the interaction between iTBS stimulation intensity and the pre- vs. post-stimulation effects on BEN and paired sample t-tests to examine the specific BEN effects of iTBS at different intensities. Additionally, spatial correlation analysis was conducted to determine whether iTBS altered the baseline coupling between BEN and neurotransmitter receptors/transporters, to investigate potential neurotransmitter changes induced by iTBS. Our results indicate that subthreshold iTBS (90 ​% rMT) reduced striatal BEN, while suprathreshold iTBS (120 ​% rMT) increased it. Subthreshold iTBS led to changes in the baseline coupling between BEN and several neurotransmitter receptor/transporter maps, primarily involving serotonin (5-HT), cannabinoid (CB), acetylcholine (ACh), and glutamate (Glu). Our findings suggest that BEN is sensitive to the effects of iTBS, with different stimulation intensities having distinct effects on neural activity. Notably, subthreshold iTBS may offer more effective stimulation. This research highlights the crucial role of stimulation intensity in modulating brain activity and lays the groundwork for future clinical studies focused on optimizing therapeutic outcomes through precise stimulation intensity.

Complexity of intrinsic brain activity in relapsing-remitting multiple sclerosis patients: patterns, association with structural damage, and clinical disability

Functional plasticity has been demonstrated in multiple sclerosis (MS) studies. However, the intrinsic brain activity complexity alterations remain unclear. Here, using a coarse-graining time-series procedure algorithm, we obtained multiscale entropy (MSE) from a retrospective multi-centre dataset (208 relapsing-remitting MS patients and 228 healthy controls). By linear mixed model analysis, we demonstrated (1) increased entropy at scale 1 and decreased entropy at scale 6, indicating that regional brain activity shifted towards randomness in the stable MS subgroups (n = 159), and (2) decreased entropy across scales 1-6, trending towards regularity in the acute MS subgroups (n = 49). The main results of the correlation analysis included the following: (1) Decreased entropy was associated with lesion volume and brain volume specifically on longer time scales (scale 3-5), and (2) increased entropy of scale 3 was associated with clinical disability scores. These findings reflect the critical role of structural disruption in the brain activity complexity of BOLD signals in MS patients.

Alterations in temporal-spatial brain entropy in treatment-resistant depression treated with nitrous oxide: Evidence from resting-state EEG

OBJECTIVE

Entropy analysis can quantify the dynamic states of the brain and reflect its information processing capacity. Nitrous oxide has shown rapid antidepressant effects in treatment-resistant depression (TRD) patients, but its biomarkers are not yet established.

METHODS

We recruited 44 TRD patients and randomly assigned them to two groups: one received a 1-hour nitrous oxide inhalation treatment, while the other received a placebo. Resting-state EEG (rs-EEG) scans were conducted at baseline and 24 h post-treatment. A novel approach based multivariate multiscale entropy (MMSE) was employed to analyze temporal-spatial brain entropy (ts-BEN) across four hierarchical brain regions.

RESULTS

TRD patients exhibited significant time-dependent increases in BEN in the frontal lobe region (sensor space: time scales 5-10; source space: time scales 1-5), changes not previously observed. Temporal-spatial BEN correlated with the severity of TRD symptoms and treatment efficacy, indicating adaptive adjustments in brain resting states.

CONCLUSION

MMSE offers a novel supplementary method for rs-EEG BEN analysis, quantifying the sensitivity of ts-BEN in monitoring nitrous oxide treatment effects. Changes in frontal region ts-BEN may serve as potential biomarkers for TRD and its treatment outcomes.

SIGNIFICANCE

Our findings enhance the understanding of the physiological mechanisms underlying nitrous oxide treatment for TRD, aiding in clinical diagnosis.

The different impacts of functional network centrality and connectivity on the complexity of brain signals in healthy control and first-episode drug-naïve patients with major depressive disorder

In recent years, brain signal complexity has gained attention as an indicator of brain well-being and a predictor of disease and dysfunction. Brain entropy quantifies this complexity. Assessment of functional network centrality and connectivity reveals that information communication induces neural signal oscillations in certain brain regions. However, their relationship is uncertain. This work studied brain signal complexity, network centrality, and connectivity in both healthy and depressed individuals. The current work comprised a sample of 124 first-episode drug-naïve patients with major depressive disorder (MDD) and 105 healthy controls (HC). Six functional networks were created for each person using resting-state functional magnetic resonance imaging. For each network, entropy, centrality, and connectivity were computed. Using structural equation modeling, this study examined the associations between brain network entropy, centrality, and connectivity. The findings demonstrated substantial correlations of entropy with both centrality and connectivity in HC and these correlation patterns were disrupted in MDD. Compared to HC, MDD exhibited higher entropy in four networks and demonstrated changes in centralities across all networks. The structural equation modeling showed that network centralities, connectivity, and depression severity had impacts on brain entropy. Nevertheless, no impacts were observed in the opposite directions. This study indicated that the complexity of brain signals was influenced not only by the interactions among different areas of the brain but also by the severity level of depression. These findings enhanced our comprehension of the associations of brain entropy with its influential factors.

rsfMRI-based brain entropy is negatively correlated with gray matter volume and surface area

The brain entropy (BEN) reflects the randomness of brain activity and is inversely related to its temporal coherence. In recent years, BEN has been found to be associated with a number of neurocognitive, biological, and sociodemographic variables such as fluid intelligence, age, sex, and education. However, evidence regarding the potential relationship between BEN and brain structure is still lacking. In this study, we use resting-state fMRI (rsfMRI) data to estimate BEN and investigate its associations with three structural brain metrics: gray matter volume (GMV), surface area (SA), and cortical thickness (CT). We performed separate analyses on BEN maps derived from four distinct rsfMRI runs, and used a voxelwise as well as a regions-of-interest (ROIs) approach. Our findings consistently showed that lower BEN was related to increased GMV and SA in the lateral frontal and temporal lobes, inferior parietal lobules, and precuneus. We hypothesize that lower BEN and higher SA might reflect higher brain reserve as well as increased information processing capacity.

Abnormal brain entropy dynamics in ADHD

BACKGROUND

Brain entropy (BEN) is a novel measure for irregularity and complexity of brain activities, which has been used to characterize abnormal brain activities in many brain disorders including attention-deficit/hyperactivity disorder (ADHD). While most research assumes BEN is stationary during scan sessions, the brain in resting state is also a highly dynamic system. The BEN dynamics in ADHD has not been explored.

METHODS

We used a sliding window approach to derive the dynamical brain entropy (dBEN) from resting-state functional magnetic resonance imaging (rfMRI) dataset that includes 98 ADHD patients and 111 healthy controls (HCs). We identified 3 reoccurring BEN states. We tested whether the BEN dynamics differ between ADHD and HC, and whether they are associated with ADHD symptom severity.

RESULTS

One BEN states, characterized by low overall BEN and low within-state BEN located in SMN (sensorimotor network) and VN (visual network), its FW (fractional window) and MDT (mean dwell time) were increased in ADHD and positively correlated with ADHD severity; another state characterized by high overall BEN and low within-state BEN located in DMN (default mode network) and ECN (executive control network), its FW and MDT were decreased in ADHD and negatively correlated with ADHD severity.

LIMITATIONS

The window length of dBEN analysis can be further optimized to suit more datasets. The co-variation between dBEN and other dynamical brain metrics was not explored.

CONCLUSION

Our findings revealed abnormal BEN dynamics in ADHD, providing new insights into clinical diagnosis and neuropathology of ADHD.

The multi-frequency decomposition entropy learning for nonlinear fMRI data analysis

Functional magnetic resonance imaging (fMRI) have been widely adopted to explore the underlying neural mechanisms between psychiatric disorders which share common neurobiology and clinical manifestations. However, the existing studies mainly focus on linear relationships and ignore nonlinear contributions. To address the above issues, we propose a new method named multi-frequency decomposition entropy (MDE) learning for inferring nonlinear functional connectivity between brain regions. Firstly, the variational mode decomposition was used to divide fMRI data into five groups of frequency. Next, the copula entropy was used to calculate the nonlinear relationship between brain regions in each frequency group, and then the best important nonlinear relationships were screen out by using statistical t-test. Lastly, a gyrus importance index was proposed to reflect the distribution trend of gyri in different frequency groups. The results of applying MDE for the fMRI data analysis of schizophrenia, bipolar disorder, and attention-deficit hyperactivity disorder showed that the difference between the three groups of patient and healthy control is large at the hub nodes, and the nonlinear relationship between the patient groups is weak when they are at the same hub node. In addition, each disease exhibits unique characteristics compared with other diseases and healthy control. In a word, the nonlinear functional connectivity of different frequency groups reflect the differences and commonalities between diseases and reveal possible discriminating biomarkers among mental diseases.

Older is order: entropy reduction in cortical spontaneous activity marks healthy aging

BACKGROUND

Entropy trajectories remain unclear for the aging process of human brain system due to the lacking of longitudinal neuroimaging resource.

RESULTS

We used open data from an accelerated longitudinal cohort (PREVENT-AD) that included 24 healthy aging participants followed by 4 years with 5 visits per participant to establish cortical entropy aging curves and distinguish with the effects of age and cohort. This reveals that global cortical entropy decreased with aging, while a significant cohort effect was detectable that people who were born earlier showed higher cortical entropy. Such entropy reductions were also evident for large-scale cortical networks, although with different rates of reduction for different networks. Specifically, the primary and intermediate networks reduce their entropy faster than the higher-order association networks.

CONCLUSIONS

Our study confirmed that cortical entropy decreases continually in the aging process, both globally and regionally, and we conclude two specific characteristics of the entropy of the human cortex with aging: the shift of the complexity hierarchy and the diversity of complexity strengthen.

Global brain connectivity: Test-retest stability and association with biological and neurocognitive variables

BACKGROUND

Global brain connectivity (GBC) enables measuring brain regions' functional connectivity strength at rest by computing the average correlation between each brain voxel's time-series and that of all other voxels.

NEW METHOD

We used resting-state fMRI (rs-fMRI) data of young adult participants from the Human Connectome Project (HCP) dataset to explore the test-retest stability of GBC, the brain regions with higher or lower GBC, as well as the associations of this measure with age, sex, and fluid intelligence. GBC was computed by considering separately the positive and negative correlation coefficients (positive GBC and negative GBC).

RESULTS

Test-retest stability was higher for positive compared to negative GBC. Areas with higher GBC were located in the default mode network, insula, and visual areas, while regions with lower GBC were in subcortical regions, temporal cortex, and cerebellum. Higher age was related to global reduction of positive GBC. Males displayed higher positive GBC in the whole brain. Fluid intelligence was associated to increased positive GBC in fronto-parietal, occipital and temporal regions.

COMPARISON WITH EXISTING METHOD

Compared to previous works, this study adopted a larger sample size and tested GBC stability using data from different rs-fMRI sessions. Moreover, these associations were examined by testing positive and negative GBC separately.

CONCLUSIONS

Lower stability for negative compared to positive GBC suggests that negative correlations may reflect less stable couplings between brain regions. Our findings indicate a greater importance of positive compared to negative GBC for the associations of functional connectivity strength with biological and neurocognitive variables.

Prevalence and characteristics of vascular cognitive impairment in a European cohort of adult patients with Moyamoya angiopathy

INTRODUCTION

Moyamoya angiopathy (MMA) is associated with a high risk of stroke, but it is also increasingly recognized as leading to cognitive impairment. The aim of this study was to determine the prevalence, nature, and severity of vascular cognitive impairment no dementia (VCIND) in adults with MMA and to identify clinical and imaging factors associated with VCIND.

METHODS

We conducted a retrospective study of consecutive adult patients with MMA followed in two tertiary hospitals (Toulouse and Paris Lariboisiere). All patients underwent neuropsychological assessment and brain magnetic resonance imaging (MRI). VCIND was defined as at least two variables of the same cognitive process with z-scores of < 2 standard deviations, regardless of the cognitive domain, that do not interfere in everyday life. Baseline demographic, clinical, and imaging data were compared between patients with and without VCIND.

RESULTS

A total of 102 patients (mean age 43 years; 65% women) were included. Thirty-four patients (33.3%) had VCIND. VCIND was mild in 20/34 (59%), moderate in 8/34 (23%), and severe in 6/34 (18%) patients. Executive function was the most widely affected (25.5%), followed by attention and processing speed (24.8%). In univariable analyses, VCIND was associated with ischemic stroke at diagnosis and the presence of ischemic lesions on MRI.

CONCLUSIONS

VCIND is highly prevalent in adults with MMA. Executive functions and processing speed are predominantly affected. These findings may guide clinicians in their evaluation of patients with MMA. Further research should assess the effect of revascularization therapies on cognitive functions.

Age-dependent functional development pattern in neonatal brain: An fMRI-based brain entropy study

The relationship between brain entropy (BEN) and early brain development has been established through animal studies. However, it remains unclear whether the BEN can be used to identify age-dependent functional changes in human neonatal brains and the genetic underpinning of the new neuroimaging marker remains to be elucidated. In this study, we analyzed resting-state fMRI data from the Developing Human Connectome Project, including 280 infants who were scanned at 37.5-43.5 weeks postmenstrual age. The BEN maps were calculated for each subject, and a voxel-wise analysis was conducted using a general linear model to examine the effects of age, sex, and preterm birth on BEN. Additionally, we evaluated the correlation between regional BEN and gene expression levels. Our results demonstrated that the BEN in the sensorimotor-auditory and association cortices, along the 'S-A' axis, was significantly positively correlated with postnatal age (PNA), and negatively correlated with gestational age (GA), respectively. Meanwhile, the BEN in the right rolandic operculum correlated significantly with both GA and PNA. Preterm-born infants exhibited increased BEN values in widespread cortical areas, particularly in the visual-motor cortex, when compared to term-born infants. Moreover, we identified five BEN-related genes (DNAJC12, FIG4, STX12, CETN2, and IRF2BP2), which were involved in protein folding, synaptic vesicle transportation and cell division. These findings suggest that the fMRI-based BEN can serve as an indicator of age-dependent brain functional development in human neonates, which may be influenced by specific genes.

The heritability and structural correlates of resting-state fMRI complexity

The complexity of fMRI signals quantifies temporal dynamics of spontaneous neural activity, which has been increasingly recognized as providing important insights into cognitive functions and psychiatric disorders. However, its heritability and structural underpinnings are not well understood. Here, we utilize multi-scale sample entropy to extract resting-state fMRI complexity in a large healthy adult sample from the Human Connectome Project. We show that fMRI complexity at multiple time scales is heritable in broad brain regions. Heritability estimates are modest and regionally variable. We relate fMRI complexity to brain structure including surface area, cortical myelination, cortical thickness, subcortical volumes, and total brain volume. We find that surface area is negatively correlated with fine-scale complexity and positively correlated with coarse-scale complexity in most cortical regions, especially the association cortex. Most of these correlations are related to common genetic and environmental effects. We also find positive correlations between cortical myelination and fMRI complexity at fine scales and negative correlations at coarse scales in the prefrontal cortex, lateral temporal lobe, precuneus, lateral parietal cortex, and cingulate cortex, with these correlations mainly attributed to common environmental effects. We detect few significant associations between fMRI complexity and cortical thickness. Despite the non-significant association with total brain volume, fMRI complexity exhibits significant correlations with subcortical volumes in the hippocampus, cerebellum, putamen, and pallidum at certain scales. Collectively, our work establishes the genetic basis and structural correlates of resting-state fMRI complexity across multiple scales, supporting its potential application as an endophenotype for psychiatric disorders.

What does it mean for consciousness to be multidimensional? A narrative review

A recent development in the psychological and neuroscientific study of consciousness has been the tendency to conceptualize consciousness as a multidimensional phenomenon. This narrative review elucidates the notion of dimensionality of consciousness and outlines the key concepts and disagreements on this topic through the viewpoints of several theoretical proposals. The reviewed literature is critically evaluated, and the main issues to be resolved by future theoretical and empirical work are identified: the problems of dimension selection and dimension aggregation, as well as some ethical considerations. This narrative review is seemingly the first to comprehensively overview this specific aspect of consciousness science.

Sex differences of signal complexity at resting-state functional magnetic resonance imaging and their associations with the estrogen-signaling pathway in the brain

Sex differences in the brain have been widely reported and may hold the key to elucidating sex differences in many medical conditions and drug response. However, the molecular correlates of these sex differences in structural and functional brain measures in the human brain remain unclear. Herein, we used sample entropy (SampEn) to quantify the signal complexity of resting-state functional magnetic resonance imaging (rsfMRI) in a large neuroimaging cohort (N = 1,642). The frontoparietal control network and the cingulo-opercular network had high signal complexity while the cerebellar and sensory motor networks had low signal complexity in both men and women. Compared with those in male brains, we found greater signal complexity in all functional brain networks in female brains with the default mode network exhibiting the largest sex difference. Using the gene expression data in brain tissues, we identified genes that were significantly associated with sex differences in brain signal complexity. The significant genes were enriched in the gene sets that were differentially expressed between the brain cortex and other tissues, the estrogen-signaling pathway, and the biological function of neural plasticity. In particular, the G-protein-coupled estrogen receptor 1 gene in the estrogen-signaling pathway was expressed more in brain regions with greater sex differences in SampEn. In conclusion, greater complexity in female brains may reflect the interactions between sex hormone fluctuations and neuromodulation of estrogen in women.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-023-09954-y.

Divergent association between pain intensity and resting-state fMRI-based brain entropy in different age groups

Pain is a multidimensional subjective experience sustained by multiple brain regions involved in different aspects of pain experience. We used brain entropy (BEN) estimated from resting-state fMRI (rsfMRI) data to investigate the neural correlates of pain experience. BEN was estimated from rs-fMRI data provided by two datasets with different age range: the Human Connectome Project-Young Adult (HCP-YA) and the Human Connectome project-Aging (HCP-A) datasets. Retrospective assessment of experienced pain intensity was retrieved from both datasets. No main effect of pain intensity was observed. The interaction between pain and age, however, was related to increased BEN in several pain-related brain regions, reflecting greater variability of spontaneous brain activity. Dividing the sample into a young adult group (YG) and a middle age-aging group (MAG) resulted in two divergent patterns of pain-BEN association: In the YG, pain intensity was related to reduced BEN in brain regions involved in the sensory processing of pain; in the MAG, pain was associated with increased BEN in areas related to both sensory and cognitive aspects of pain experience.

Associations of Brain Entropy Estimated by Resting State fMRI With Physiological Indices, Body Mass Index, and Cognition

BACKGROUND

In recent years, resting-state fMRI (rsfMRI)-based brain entropy (BEN) has gained increasing interest as a tool to characterize brain activity. While previous studies indicate that BEN is correlated with cognition, it remains unclear whether BEN is influenced by other factors that typically affect brain activity measured by fMRI.

PURPOSE

To investigate the relationship between BEN and physiological indices, including respiratory rate (RR), heart rate (HR), systolic blood pressure (s-BP), and body mass index (BMI), and to investigate whether and to what extent the relationship between BEN and cognition is influenced by physiological variables.

STUDY TYPE

Retrospective.

SUBJECTS

One thousand two hundred six healthy subjects (mean age: 28.83 ± 3.69 years; 550 male) with rsfMRI datasets selected from the Human Connectome Project (HCP).

FIELD STRENGTH/SEQUENCE

Multiband echo planar imaging (EPI) sequence at 3.0 Tesla.

ASSESSMENT

Neurocognitive, physical health (RR, HR, s-BP, BMI), and rsfMRI data were retrieved from the HCP datasets. Neurocognition was measured through the total cognition composite (TCC) score provided by HCP. BEN maps were calculated from rsfMRI data.

STATISTICAL TESTS

Multiple regression models, pheight-family wise error (FWE) < 0.05 and pcluster-FWE < 0.05 were considered statistically significant.

RESULTS

BEN was negatively associated with RR (T-thresholds ranging from 4.75 to 4.8; r-threshold = |0.15|) and positively associated with s-BP and BMI (T-thresholds ranging from 4.75 to 4.8; r-threshold = |0.15|) in areas overlapping with the default mode network. After controlling the physiological effects, BEN still showed regional associations with TCC, including negative associations (T-thresholds = 3.09; r-threshold = |0.1|) in the fronto-parietal cortex and positive associations (T-thresholds = 3.09; r-threshold = |0.1|) in the sensorimotor system (motor network and the limbic system).

DATA CONCLUSIONS

RR negatively affects rsfMRI-derived BEN, while s-BP and BMI positively affect BEN. The positive associations between BEN and cognition in the motor network and the limbic system might indicate a facilitation of information processing in the sensorimotor system.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 3.

Cognitive protection and brain entropy changes from omega-3 polyunsaturated fatty acids supplement in late-life depression: A 52-week randomized controlled trial

BACKGROUND

Late-life depression (LLD) is associated with risk of dementia, yet intervention of LLD provides an opportunity to attenuate subsequent cognitive decline. Omega-3 polyunsaturated fatty acids (PUFAs) supplement is a potential intervention due to their beneficial effect in depressive symptoms and cognitive function. To explore the underlying neural mechanism, we used resting-state functional MRI (rs-fMRI) before and after omega-3 PUFAs supplement in older adults with LLD.

METHODS

A 52-week double-blind randomized controlled trial was conducted. We used multi-scale sample entropy to analyze rs-fMRI data. Comprehensive cognitive tests and inflammatory markers were collected to correlate with brain entropy changes.

RESULTS

A total of 20 patients completed the trial with 11 under omega-3 PUFAs and nine under placebo. While no significant global cognitive improvement was observed, a marginal enhancement in processing speed was noted in the omega-3 PUFAs group. Importantly, participants receiving omega-3 PUFAs exhibited decreased brain entropy in left posterior cingulate gyrus (PCG), multiple visual areas, the orbital part of the right middle frontal gyrus, and the left Rolandic operculum. The brain entropy changes of the PCG in the omega-3 PUFAs group correlated with improvement of language function and attenuation of interleukin-6 levels.

LIMITATIONS

Sample size is small with only marginal clinical effect.

CONCLUSION

These findings suggest that omega-3 PUFAs supplement may mitigate cognitive decline in LLD through anti-inflammatory mechanisms and modulation of brain entropy. Larger clinical trials are warranted to validate the potential therapeutic implications of omega-3 PUFAs for deterring cognitive decline in patients with late-life depression.

Cross-subject brain entropy mapping

We present a method to map the regional similarity between resting state fMRI activities of different individuals. The similarity was measured using cross-entropy. Group level patterns were displayed based on the Human Connectome Project Youth data. While we only showed the cross-subject brain entropy (BEN) mapping results in this manuscript, the same concept can be directly extended to map the cross-sessional BEN and the cross-regional cross-subject or subject-session BEN.

The brain entropy dynamics in resting state

As a novel measure for irregularity and complexity of the spontaneous fluctuations of brain activities, brain entropy (BEN) has attracted much attention in resting-state functional magnetic resonance imaging (rs-fMRI) studies during the last decade. Previous studies have shown its associations with cognitive and mental functions. While most previous research assumes BEN is approximately stationary during scan sessions, the brain, even at its resting state, is a highly dynamic system. Such dynamics could be characterized by a series of reoccurring whole-brain patterns related to cognitive and mental processes. The present study aims to explore the time-varying feature of BEN and its potential links with general cognitive ability. We adopted a sliding window approach to derive the dynamical brain entropy (dBEN) of the whole-brain functional networks from the HCP (Human Connectome Project) rs-fMRI dataset that includes 812 young healthy adults. The dBEN was further clustered into 4 reoccurring BEN states by the k-means clustering method. The fraction window (FW) and mean dwell time (MDT) of one BEN state, characterized by the extremely low overall BEN, were found to be negatively correlated with general cognitive abilities (i.e., cognitive flexibility, inhibitory control, and processing speed). Another BEN state, characterized by intermediate overall BEN and low within-state BEN located in DMN, ECN, and part of SAN, its FW, and MDT were positively correlated with the above cognitive abilities. The results of our study advance our understanding of the underlying mechanism of BEN dynamics and provide a potential framework for future investigations in clinical populations.

Task-induced changes in brain entropy

Entropy indicates irregularity of a dynamic system, with higher entropy indicating higher irregularity and more transit states. In the human brain, regional brain entropy (BEN) has been increasingly assessed using resting state fMRI (rs-fMRI), while changes of regional BEN during task-based fMRI have been scarcely studied. The purpose of this study is to characterize task-induced regional BEN alterations using the large Human Connectome Project (HCP) data. To control the potential modulation by the block design, BEN of task-fMRI was calculated from the fMRI images acquired during the task conditions only (task BEN) and then compared to BEN of rs-fMRI (resting BEN). Moreover, BEN was separately calculated from the control blocks of the task-fMRI runs (control BEN) and compared to task BEN. Finally, control BEN was compared to resting BEN to test for residual task effects in the control condition. With respect to resting state, task performance unanimously induced BEN reduction in the peripheral cortical area and BEN increase in the centric part of the sensorimotor and perception networks. Control compared to resting BEN showed similar entropy alterations, suggesting large residual task effects. Task compared to control BEN was characterized by reduced entropy in occipital, orbitofrontal, and parietal regions.

Predicting suicidality in late-life depression by 3D convolutional neural network and cross-sample entropy analysis of resting-state fMRI

BACKGROUND

Predicting suicide is a pressing issue among older adults; however, predicting its risk is difficult. Capitalizing on the recent development of machine learning, considerable progress has been made in predicting complex behavior such as suicide. As depression remained the strongest risk for suicide, we aimed to apply deep learning algorithms to identify suicidality in a group with late-life depression (LLD).

METHODS

We enrolled 83 patients with LLD, 35 of which were non-suicidal and 48 were suicidal, including 26 with only suicidal ideation and 22 with past suicide attempts, for resting-state functional magnetic resonance imaging (MRI). Cross-sample entropy (CSE) analysis was conducted to examine the complexity of MRI signals among brain regions. Three-dimensional (3D) convolutional neural networks (CNNs) were used, and the classification accuracy in each brain region was averaged to predict suicidality after sixfold cross-validation.

RESULTS

We found brain regions with a mean accuracy above 75% to predict suicidality located mostly in default mode, fronto-parietal, and cingulo-opercular resting-state networks. The models with right amygdala and left caudate provided the most reliable accuracy in all cross-validation folds, indicating their neurobiological importance in late-life suicide.

CONCLUSION

Combining CSE analysis and the 3D CNN, several brain regions were found to be associated with suicidality.

Common hyper-entropy patterns identified in nicotine smoking, marijuana use, and alcohol use based on uni-drug dependence cohorts

Substance use disorders present similar behaviors and psychopathologies related to impaired decision making/inhibition control and information processing, suggesting common alterations in frontal and limbic brain areas. To test this hypothesis, we identified three uni-substance use cohorts with dependence to only one type of substance from the Human Connectome Project: marijuana dependence, nicotine dependence, and alcohol dependence. Fifty-nine marijuana uses, 34 nicotine smokers, 35 alcohol drinkers, and their age and sex-matched non-substance use controls were identified. We used brain entropy mapping to probe brain alterations in substance use disorders. Compared to non-substance use individuals, all three substance use disorder cohorts had increased brain entropy. Marijuana dependence and nicotine dependence showed overlapped hyper-brain entropy in bilateral dorso-lateral prefrontal cortex, anterior cingulate cortex, and right insula. Hyper-brain entropy in marijuana dependence and alcohol dependence overlap in left insula, left doso-lateral prefrontal cortex, and posterior cingulate. Hyper-brain entropy in nicotine dependence and alcohol dependence overlap only in left dorso-lateral prefrontal cortex. Hyper-brain entropy in those areas was correlated with increased impulsivity or reduced inhibition control in substance use disorder but not in controls. Drug dependence is associated with hyper-brain entropy in the prefrontal cortex and the meso-limbic system, independent of a specific addictive drug. Brain entropy in this circuit provides a sensitive marker to detect brain and behavioral alterations in substance user disorders.

Multivariate multiscale entropy (mMSE) as a tool for understanding the resting-state EEG signal dynamics: the spatial distribution and sex/gender-related differences

BACKGROUND

The study aimed to determine how the resting-state EEG (rsEEG) complexity changes both over time and space (channels). The complexity of rsEEG and its sex/gender differences were examined using the multivariate Multiscale Entropy (mMSE) in 95 healthy adults. Following the probability maps (Giacometti et al. in J Neurosci Methods 229:84-96, 2014), channel sets have been identified that correspond to the functional networks. For each channel set the area under curve (AUC), which represents the total complexity, MaxSlope-the maximum complexity change of the EEG signal at thefine scales (1:4 timescales), and AvgEnt-to the average entropy level at coarse-grained scales (9:12 timescales), respectively, were extracted. To check dynamic changes between the entropy level at the fine and coarse-grained scales, the difference in mMSE between the #9 and #4 timescale (DiffEnt) was also calculated.

RESULTS

We found the highest AUC for the channel sets corresponding to the somatomotor (SMN), dorsolateral network (DAN) and default mode (DMN) whereas the visual network (VN), limbic (LN), and frontoparietal (FPN) network showed the lowest AUC. The largest MaxSlope were in the SMN, DMN, ventral attention network (VAN), LN and FPN, and the smallest in the VN. The SMN and DAN were characterized by the highest and the LN, FPN, and VN by the lowest AvgEnt. The most stable entropy were for the DAN and VN while the LN showed the greatest drop of entropy at the coarse scales. Women, compared to men, showed higher MaxSlope and DiffEnt but lower AvgEnt in all channel sets.

CONCLUSIONS

Novel results of the present study are: (1) an identification of the mMSE features that capture entropy at the fine and coarse timescales in the channel sets corresponding to the main resting-state networks; (2) the sex/gender differences in these features.

Immediate visual reproduction negatively correlates with brain entropy of parahippocampal gyrus and inferior occipital gyrus in bipolar II disorder adolescents

BACKGROUND

Brain entropy reveals complexity and irregularity of brain, and it has been proven to reflect brain complexity alteration in disease states. Previous studies found that bipolar disorder adolescents showed cognitive impairment. The relationship between complexity of brain neural activity and cognition of bipolar II disorder (BD-II) adolescents remains unclear.

METHODS

Nineteen BD-II patients (14.63 ±1.57 years old) and seventeen age-gender matched healthy controls (HCs) (14.18 ± 1.51 years old) were enlisted. Entropy values of all voxels of the brain in resting-state functional MRI data were calculated and differences of them between BD-II and HC groups were evaluated. After that, correlation analyses were performed between entropy values of brain regions showing significant entropy differences and clinical indices in BD-II adolescents.

RESULTS

Significant differences were found in scores of immediate visual reproduction subtest (VR-I, p = 0.003) and Stroop color-word test (SCWT-1, p = 0.015; SCWT-2, p = 0.004; SCWT-3, p = 0.003) between the two groups. Compared with HCs, BD-II adolescents showed significant increased brain entropy in right parahippocampal gyrus and right inferior occipital gyrus. Besides, significant negative correlations between brain entropy values of right parahippocampal gyrus, right inferior occipital gyrus and immediate visual reproduction subtest scores were observed in BD-II adolescents.

CONCLUSIONS

The findings of the present study suggested that the disrupted function of corticolimbic system is related with cognitive abnormality of BD-II adolescents. And from the perspective temporal dynamics of brain system, the current study, brain entropy may provide available evidences for understanding the underlying neural mechanism in BD-II adolescents.

Aberrant brain entropy in posttraumatic stress disorder comorbid with major depressive disorder during the coronavirus disease 2019 pandemic

Aim

Previously, neuroimaging studies on comorbid Posttraumatic-Major depression disorder (PTSD-MDD) comorbidity found abnormalities in multiple brain regions among patients. Recent neuroimaging studies have revealed dynamic nature on human brain activity during resting state, and entropy as an indicator of dynamic regularity may provide a new perspective for studying abnormalities of brain function among PTSD-MDD patients. During the COVID-19 pandemic, there has been a significant increase in the number of patients with PTSD-MDD. We have decided to conduct research on resting-state brain functional activity of patients who developed PTSD-MDD during this period using entropy.

Methods

Thirty three patients with PTSD-MDD and 36 matched TCs were recruited. PTSD and depression symptoms were assessed using multiple clinical scales. All subjects underwent functional magnetic resonance imaging (fMRI) scans. And the brain entropy (BEN) maps were calculated using the BEN mapping toolbox. A two-sample t-test was used to compare the differences in the brain entropy between the PTSD-MDD comorbidity group and TC group. Furthermore, correlation analysis was conducted between the BEN changes in patients with PTSD-MDD and clinical scales.

Results

Compared to the TCs, PTSD-MDD patients had a reduced BEN in the right middle frontal orbital gyrus (R_MFOG), left putamen, and right inferior frontal gyrus, opercular part (R_IFOG). Furthermore, a higher BEN in the R_MFOG was related to higher CAPS and HAMD-24 scores in the patients with PTSD-MDD.

Conclusion

The results showed that the R_MFOG is a potential marker for showing the symptom severity of PTSD-MDD comorbidity. Consequently, PTSD-MDD may have reduced BEN in frontal and basal ganglia regions which are related to emotional dysregulation and cognitive deficits.

Task-induced changes in brain entropy

Entropy indicates irregularity of a dynamic system with higher entropy indicating higher irregularity and more transit states. In the human brain, regional entropy has been increasingly assessed using resting state fMRI. Response of regional entropy to task has been scarcely studied. The purpose of this study is to characterize task-induced regional brain entropy (BEN) alterations using the large Human Connectome Project (HCP) data. To control the potential modulation by the block-design, BEN of task-fMRI was calculated from the fMRI images acquired during the task conditions only and then compared to BEN of rsfMRI. Compared to resting state, task-performance unanimously induced BEN reduction in the peripheral cortical area including both the task activated regions and task non-specific regions such as the task negative area and BEN increase in the centric part of the sensorimotor and perception networks. Task control condition showed large residual task effects. After controlling the task non-specific effects using the control BEN vs task BEN comparison, regional BEN showed task specific effects in target regions.

From local properties to brain-wide organization: A review of intraregional temporal features in functional magnetic resonance imaging data

Based on the fluctuations ensembled over neighbouring neurons, blood oxygen level-dependent (BOLD) signal is a mesoscale measurement of brain signals. Intraregional temporal features (IRTFs) of BOLD signal, extracted from regional neural activities, are utilized to investigate how the brain functions in local brain areas. This literature highlights four types of IRTFs and their representative calculations including variability in the temporal domain, variability in the frequency domain, entropy, and intrinsic neural timescales, which are tightly related to cognitions. In the brain-wide spatial organization, these brain features generally organized into two spatial hierarchies, reflecting structural constraints of regional dynamics and hierarchical functional processing workflow in brain. Meanwhile, the spatial organization gives rise to the link between neuronal properties and cognitive performance. Disrupted or unbalanced spatial conditions of IRTFs emerge with suboptimal cognitive states, which improved our understanding of the aging process and/or neuropathology of brain disease. This review concludes that IRTFs are important properties of the brain functional system and IRTFs should be considered in a brain-wide manner.

Many Paths to Alzheimer's Disease: A Unifying Hypothesis Integrating Biological, Chemical, and Physical Risk Factors

Sporadic Alzheimer's disease (AD) is a complex, multifactorial disease. We should therefore expect to find many factors involved in its causation. The known neuropathology seen at autopsy in patients dying with AD is not consistently seen in all patients with AD and is sometimes seen in patients without dementia. This suggests that patients follow different paths to AD, with different people having slightly different combinations of predisposing physical, chemical and biologic risk factors, and varying neuropathology. This review summarizes what is known of the biologic and chemical predisposing factors and features in AD. We postulate that, underlying the neuropathology of AD is a progressive failure of neurons, with advancing age or other morbidity, to rid themselves of entropy, i.e., the disordered state resulting from brain metabolism. Understanding the diverse causes of AD may allow the development of new therapies targeted at blocking the paths that lead to dementia in each subset of patients.

More than just statics: Altered complexity of dynamic amplitude of low-frequency fluctuations in the resting brain after stroke

OBJECTIVE

Previous neuroimaging studies mainly focused on static characteristics of brain activity, and little is known about its characteristics over time, especially in post-stroke (PS) patients. In this study, we aimed to investigate the static and dynamic characteristics of brain activity after stroke using functional magnetic resonance imaging (fMRI).

APPROACH

Twenty ischemic PS patients and nineteen healthy controls (HCs) were recruited to receive a resting-state fMRI scanning. The static amplitude of low-frequency fluctuations (sALFF) and fuzzy entropy of dynamic ALFF (FE-dALFF) were applied to identify the stroke-induced alterations.

MAIN RESULTS

Compared with the HCs, PS patients showed significantly increased FE-dALFF values in the right angular gyrus (ANG), bilateral precuneus (PCUN), and right inferior parietal lobule (IPL) as well as significantly decreased FE-dALFF values in the right postcentral gyrus (PoCG), right dorsolateral superior frontal gyrus (SFGdor), and right precentral gyrus (PreCG). The ROC analyses demonstrated that FE-dALFF and sALFF possess comparable sensitivity in distinguishing PS patients from the HCs. Moreover, a significantly positive correlation was observed between the FE-dALFF values and the Fugl-Meyer Assessment (FMA) scores in the right SFGdor (r =0.547), right IPL (r =0.522), and right PCUN (r =0.486).

SIGNIFICANCE

This study provided insight into the stroke-induced alterations in static and dynamic characteristics of local brain activity, highlighting the potential of FE-dALFF in understanding neurophysiological mechanisms and evaluating pathological changes.

Lower resting brain entropy is associated with stronger task activation and deactivation

Brain entropy (BEN) calculated from resting state fMRI has been the subject of increasing research interest in recent years. Previous studies have shown the correlations between rest BEN and neurocognition and task performance, but how this relates to task-evoked brain activations and deactivations remains unknown. The purpose of this study is to address this open question using large data (n = 862). Voxel wise correlations were calculated between rest BEN and task activations/deactivations of five different tasks. For most of the assessed tasks, lower rest BEN was found to be associated with stronger activations (negative correlations) and stronger deactivations (positive correlations) only in brain regions activated or deactivated by the tasks. Higher workload evoked spatially more extended negative correlations between rest BEN and task activations. These results not only confirm that resting brain activity can predict brain activity during task performance but also for the first time show that resting brain activity may facilitate both task activations and deactivations. In addition, the results provide a clue to understanding the individual differences of task performance and brain activations.