A Brainnetome Atlas Based Mild Cognitive Impairment Identification Using Hurst Exponent

Multi-Threshold Recurrence Rate Plot: A Novel Methodology for EEG Analysis in Alzheimer's Disease and Frontotemporal Dementia

This study introduces Multi-Threshold Recurrence Rate Plots (MTRRP), a novel methodology for analyzing dynamic patterns in complex systems, such as those influenced by neurodegenerative diseases in brain activity. MTRRP characterizes how recurrence rates evolve with increasing recurrence thresholds. A key innovation of our approach, Recurrence Complexity, captures structural complexity by integrating local randomness and global structural features through the product of Recurrence Rate Gradient and Recurrence Hurst, both derived from MTRRP. We applied this technique to resting-state EEG data from patients diagnosed with Alzheimer's Disease (AD), Frontotemporal Dementia (FTD), and age-matched healthy controls. The results revealed significantly higher recurrence complexity in the occipital areas of AD and FTD patients, particularly pronounced in the Alpha and Beta frequency bands. Furthermore, EEG features derived from MTRRP were evaluated using a Support Vector Machine with leave-one-out cross-validation, achieving a classification accuracy of 87.7%. These findings not only underscore the utility of MTRRP in detecting distinct neurophysiological patterns associated with neurodegenerative diseases but also highlight its broader applicability in time series analysis, providing a substantial tool for advancing medical diagnostics and research.

Improvements in task performance after practice are associated with scale-free dynamics of brain activity

Although practicing a task generally benefits later performance on that same task, there are individual differences in practice effects. One avenue to model such differences comes from research showing that brain networks extract functional advantages from operating in the vicinity of criticality, a state in which brain network activity is more scale-free. We hypothesized that higher scale-free signal from fMRI data, measured with the Hurst exponent (H), indicates closer proximity to critical states. We tested whether individuals with higher H during repeated task performance would show greater practice effects. In Study 1, participants performed a dual-n-back task (DNB) twice during MRI (n = 56). In Study 2, we used two runs of n-back task (NBK) data from the Human Connectome Project sample (n = 599). In Study 3, participants performed a word completion task (CAST) across six runs (n = 44). In all three studies, multivariate analysis was used to test whether higher H was related to greater practice-related performance improvement. Supporting our hypothesis, we found patterns of higher H that reliably correlated with greater performance improvement across participants in all three studies. However, the predictive brain regions were distinct, suggesting that the specific spatial H↑ patterns are not task-general.

A pattern of cognitive resource disruptions in childhood psychopathology

The Hurst exponent (H) isolated in fractal analyses of neuroimaging time series is implicated broadly in cognition. Within this literature, H is associated with multiple mental disorders, suggesting that H is transdimensionally associated with psychopathology. Here, we unify these results and demonstrate a pattern of decreased H with increased general psychopathology and attention-deficit/hyperactivity factor scores during a working memory task in 1,839 children. This pattern predicts current and future cognitive performance in children and some psychopathology in 703 adults. This pattern also defines psychological and functional axes associating psychopathology with an imbalance in resource allocation between fronto-parietal and sensorimotor regions, driven by reduced resource allocation to fronto-parietal regions. This suggests the hypothesis that impaired working memory function in psychopathology follows from a reduced cognitive resource pool and a reduction in resources allocated to the task at hand.

Identifying Alzheimer's disease and mild cognitive impairment with atlas-based multi-modal metrics

Introduction

Multi-modal neuroimaging metrics in combination with advanced machine learning techniques have attracted more and more attention for an effective multi-class identification of Alzheimer's disease (AD), mild cognitive impairment (MCI) and health controls (HC) recently.

Methods

In this paper, a total of 180 subjects consisting of 44 AD, 66 MCI and 58 HC subjects were enrolled, and the multi-modalities of the resting-state functional magnetic resonance imaging (rs-fMRI) and the structural MRI (sMRI) for all participants were obtained. Then, four kinds of metrics including the Hurst exponent (HE) metric and bilateral hippocampus seed independently based connectivity metrics generated from fMRI data, and the gray matter volume (GMV) metric obtained from sMRI data, were calculated and extracted in each region of interest (ROI) based on a newly proposed automated anatomical Labeling (AAL3) atlas after data pre-processing. Next, these metrics were selected with a minimal redundancy maximal relevance (MRMR) method and a sequential feature collection (SFC) algorithm, and only a subset of optimal features were retained after this step. Finally, the support vector machine (SVM) based classification methods and artificial neural network (ANN) algorithm were utilized to identify the multi-class of AD, MCI and HC subjects in single modal and multi-modal metrics respectively, and a nested ten-fold cross-validation was utilized to estimate the final classification performance.

Results

The results of the SVM and ANN based methods indicated the best accuracies of 80.36 and 74.40%, respectively, by utilizing all the multi-modal metrics, and the optimal accuracies for AD, MCI and HC were 79.55, 78.79 and 82.76%, respectively, in the SVM based method. In contrast, when using single modal metric, the SVM based method obtained a best accuracy of 72.62% with the HE metric, and the accuracies for AD, MCI and HC subjects were just 56.82, 80.30 and 75.86%, respectively. Moreover, the overlapping abnormal brain regions detected by multi-modal metrics were mainly located at posterior cingulate gyrus, superior frontal gyrus and cuneus.

Conclusion

Taken together, the SVM based method with multi-modal metrics could provide effective diagnostic information for identifying AD, MCI and HC subjects.

A Multi-Modal and Multi-Atlas Integrated Framework for Identification of Mild Cognitive Impairment

Background: Multi-modal neuroimaging with appropriate atlas is vital for effectively differentiating mild cognitive impairment (MCI) from healthy controls (HC). Methods: The resting-state functional magnetic resonance imaging (rs-fMRI) and structural MRI (sMRI) of 69 MCI patients and 61 HC subjects were collected. Then, the gray matter volumes obtained from the sMRI and Hurst exponent (HE) values calculated from rs-fMRI data in the Automated Anatomical Labeling (AAL-90), Brainnetome (BN-246), Harvard–Oxford (HOA-112) and AAL3-170 atlases were extracted, respectively. Next, these characteristics were selected with a minimal redundancy maximal relevance algorithm and a sequential feature collection method in single or multi-modalities, and only the optimal features were retained after this procedure. Lastly, the retained characteristics were served as the input features for the support vector machine (SVM)-based method to classify MCI patients, and the performance was estimated with a leave-one-out cross-validation (LOOCV). Results: Our proposed method obtained the best 92.00% accuracy, 94.92% specificity and 89.39% sensitivity with the sMRI in AAL-90 and the fMRI in HOA-112 atlas, which was much better than using the single-modal or single-atlas features. Conclusion: The results demonstrated that the multi-modal and multi-atlas integrated method could effectively recognize MCI patients, which could be extended into various neurological and neuropsychiatric diseases.

Functional Integrity of Executive Control Network Contributed to Retained Executive Abilities in Mild Cognitive Impairment

Background: Mild cognitive impairment (MCI) is considered to be a transitional state between normal aging and Alzheimer's dementia (AD). Recent studies have indicated that executive function (EF) declines during MCI. However, only a limited number of studies have investigated the neural basis of EF deficits in MCI. Herein, we investigate the changes of regional brain spontaneous activity and functional connectivity (FC) of the executive control network (ECN) between high EF and low EF groups. Methods: According to EF composite score (ADNI-EF) from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we divided MCI into two groups, including the MCI-highEF group and MCI-lowEF group. Resting-state functional MRI was utilized to investigate the fractional amplitude of low-frequency fluctuation (fALFF) and ECN functional connectivity across 23 healthy controls (HC), 11 MCI-highEF, and 14 MCI-lowEF participants. Moreover, a partial correlation analysis was carried out to examine the relationship between altered fALFF or connectivity of the ECN and the ADNI-EF. Results: Compared to HC, the MCI-highEF participants demonstrated increased fALFF in the left superior temporal gyrus (STG), as well as decreased fALFF in the right precentral gyrus, right postcentral gyrus, and left middle frontal gyrus (MFG). The MCI-lowEF participants demonstrated increased fALFF in the cerebellar vermis and decreased fALFF in the left MFG. Additionally, compared to HC, the MCI-highEF participants indicated no significant difference in connectivity of the ECN. Furthermore, the MCI-lowEF participants showed increased ECN FC in the left cuneus and left MFG, as well as decreased ECN functional connectivity in the right parahippocampal gyrus (PHG). Notably, the altered fALFF in the left MFG was positively correlated to ADNI-EF, while the altered fALFF in cerebellar vermis is negatively correlated with ADNI-EF across the two MCI groups and the HC group. Altered ECN functional connectivity in the right PHG is negatively correlated to ADNI-EF, while altered ECN functional connectivity in the left cuneus is negatively correlated to ADNI-EF across the three groups. Conclusions: Our current study demonstrates the presence of different patterns of regional brain spontaneous activity and ECN FC in the MCI-highEF group and MCI-lowEF group. Furthermore, the ECN FC of the MCI-highEF group was not disrupted, which may contribute to retained EF in MCI.

Regional Hurst Exponent Reflects Impulsivity-Related Alterations in Fronto-Hippocampal Pathways Within the Waiting Impulsivity Network

In general, the Hurst exponent. is used as a measure of long-term memory of time series. In previous neuroimaging studies, H has been introduced as one important parameter to define resting-state networks, reflecting upon global scale-free properties emerging from a network. H has been examined in the waiting impulsivity (WI) network in an earlier study. We found that alterations of H in the anterior cingulate cortex (H^ACC) and the nucleus accumbens (H^NAcc) were lower in high impulsive (highIMP) compared to low impulsive (lowIMP) participants. Following up on those findings, we addressed the relation between altered fractality in H^ACC and H^NAcc and brain activation and neural network connectivity. To do so, brain activation maps were calculated, and network connectivity was determined using the Dynamic Causal Modeling (DCM) approach. Finally, 1–H scores were determined to quantify the alterations of H. This way, the focus of the analyses was placed on the potential effects of alterations of H on neural network activation and connectivity. Correlation analyses between the alterations of H^ACC/H^NAcc and activation maps and DCM estimates were performed. We found that the alterations of H predominantly correlated with fronto-hippocampal pathways and correlations were significant only in highIMP subjects. For example, alterations of H^ACC was associated with a decrease in neural activation in the right HC in combination with increased ACC-hippocampal connectivity. Alteration inH^NAcc, in return, was related to an increase in bilateral prefrontal activation in combination with increased fronto-hippocampal connectivity. The findings, that the WI network was related to H alteration in highIMP subjects indicated that impulse control was not reduced per se but lacked consistency. Additionally, H has been used to describe long-term memory processes before, e.g., in capital markets, energy future prices, and human memory. Thus, current findings supported the relation of H toward memory processing even when further prominent cognitive functions were involved.

Mechanism differences between typical yin and typical yang personality individuals assessed by Five-Pattern Personality Inventory (FPPI): Evidence from resting-state brain functional networks

BACKGROUND

Most studies assessing brain-personality mechanisms have used Western personality questionnaires. However, Western personality questionnaires may not objectively reflect the personality characteristics of individuals in Eastern cultures such as China. Hence, we adopted the functional magnetic resonance imaging (fMRI) and the Chinese localized scale, FPPI, to explore the brain mechanisms differences of typical yin and typical yang personalities of individuals in China.

METHODS

30 typical yin personality participants (TYI) and 34 typical yang personality participants (TYA) were enrolled according to the FPPI. The group differences of the functional brain networks among 90 specific brain regions were mapped using fMRI data and then analyzed by the conventional network metrics (CNM) and frequency subgraph mining (FSM).

RESULTS

The CNM and FSM differences between two typical personality groups were traced to the frontal, temporal, and parietal cortices. The yin group, reflecting the rich emotions and feelings of individuals, showed higher betweenness centrality (BCi) and nodal efficiency (Ei) values in putamen and middle frontal gyrus. The yang group, reflecting active behaviors and tendency to adapting to the changing surroundings, showed higher BCi and Ei values in precuneus, posterior cingulate gyrus, and inferior parietal lobule, brain areas in the default mode network (DMN).

CONCLUSION

These results supplied evidence for the neurobiological differences between typical yin and typical yang personality participants based on Chinese culture. These results also provide a new perspective to help researchers understand brain mechanism differences between yin and yang personality groups in the Chinese culture.

A Comparative Atlas-Based Recognition of Mild Cognitive Impairment With Voxel-Based Morphometry

An accurate and reliable brain partition atlas is vital to quantitatively investigate the structural and functional abnormalities in mild cognitive impairment (MCI), generally considered to be a prodromal phase of Alzheimer's disease. In this paper, we proposed an automated structural classification method to identify MCI from healthy controls (HC) and investigated whether the classification performance was dependent on the brain parcellation schemes, including Automated Anatomical Labeling (AAL-90) atlas, Brainnetome (BN-246) atlas, and AAL-1024 atlas. In detail, structural magnetic resonance imaging (sMRI) data of 69 MCI patients and 63 HC matched well on gender, age, and education level were collected and analyzed with voxel-based morphometry method first, then the volume features of every region of interest (ROI) belonging to the above-mentioned three atlases were calculated and compared between MCI and HC groups, respectively. At last, the abnormal volume features were selected as the classification features for a proposed support vector machine based identification method. After the leave-one-out cross-validation to estimate the classification performance, our results reported accuracies of 83, 92, and 89% with AAL-90, BN-246, and AAL-1024 atlas, respectively, suggesting that future studies should pay more attention to the selection of brain partition schemes in the atlas-based studies. Furthermore, the consistent atrophic brain regions among three atlases were predominately located at bilateral hippocampus, bilateral parahippocampal, bilateral amygdala, bilateral cingulate gyrus, left angular gyrus, right superior frontal gyrus, right middle frontal gyrus, left inferior frontal gyrus, and left precentral gyrus.

Weighted Random Support Vector Machine Clusters Analysis of Resting-State fMRI in Mild Cognitive Impairment

The identification of abnormal cognitive decline at an early stage becomes an increasingly significant conundrum to physicians and is of major interest in the studies of mild cognitive impairment (MCI). Support vector machine (SVM) as a high-dimensional pattern classification technique is widely employed in neuroimaging research. However, the application of a single SVM classifier may be difficult to achieve the excellent classification performance because of the small-sample size and noise of imaging data. To address this issue, we propose a novel method of the weighted random support vector machine cluster (WRSVMC) in which multiple SVMs were built and different weights were given to corresponding SVMs with different classification performances. We evaluated our algorithm on resting state functional magnetic resonance imaging (RS-fMRI) data of 93 MCI patients and 105 healthy controls (HC) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. The maximum accuracy given by the WRSVMC is 87.67%, demonstrating excellent diagnostic power. Furthermore, the most discriminative brain areas have been found out as follows: gyrus rectus (REC.L), precentral gyrus (PreCG.R), olfactory cortex (OLF.L), and middle occipital gyrus (MOG.R). These findings of the paper provide a new perspective for the clinical diagnosis of MCI.