Decreased Resting-State Functional Complexity in Elderly with Subjective Cognitive Decline

Dynamic brain entropy predicts risky decision-making across transdiagnostic dimensions of psychopathology

OBJECTIVES

Maladaptive risky decision-making is a common pathological behavior among patients with various psychiatric disorders. Brain entropy, which measures the complexity of brain time series signals, provides a novel approach to assessing brain health. Despite its potential, the dynamics of brain entropy have seldom been explored. This study aimed to construct a dynamic model of brain entropy and examine its predictive value for risky decision-making in patients with mental disorders, utilizing resting-state functional magnetic resonance imaging (rs-fMRI).

METHODS

This study analyzed the rs-fMRI data from a total of 198 subjects, including 48 patients with bipolar disorder (BD), 47 patients with schizophrenia (SZ), 40 patients with adult attention deficit hyperactivity disorder (ADHD), as well as 63 healthy controls (HC). Time series signals were extracted from 264 brain regions based on rs-fMRI. The traditional static entropy and dynamic entropy (coefficient of variation, CV; rate of change, Rate) were constructed, respectively. Support vector regression was employed to predict risky decision-making utilizing leave-one-out cross-validation within each group.

RESULTS

Our findings showed that CV achieved the best performances in HC and BD groups (r = -0.58, MAE = 6.43, R2 = 0.32; r = -0.78, MAE = 12.10, R2 = 0.61), while the Rate achieved the best in SZ and ADHD groups (r = -0.69, MAE = 10.20, R2 = 0.47; r = -0.78, MAE = 7.63, R2 = 0.60). For the dynamic entropy, the feature selection threshold rather than the time window length and overlapping ratio influenced predictive performance.

CONCLUSIONS

These results suggest that dynamic brain entropy could be a more effective predictor of risky decision-making than traditional static brain entropy. Our findings offer a novel perspective on exploring brain signal complexity and can serve as a reference for interventions targeting risky decision-making behaviors, particularly in individuals with psychiatric diagnoses.

Accurate identification of individuals with subjective cognitive decline using 3D regional fractal dimensions on structural magnetic resonance imaging

BACKGROUND AND OBJECTIVE

Accurate identification of individuals with subjective cognitive decline (SCD) is crucial for early intervention and prevention of neurodegenerative diseases. Fractal dimensionality (FD) has emerged as a robust and replicable measure, surpassing traditional geometric metrics, in characterizing the intricate fractal geometrical properties of brain structure. Nevertheless, the effectiveness of FD in identifying individuals with SCD remains largely unclear. A 3D regional FD method can be suggested to characterize and quantify the spatial complexity of the precise gray matter, providing insights into cognitive aging and aiding in the automated identification of individuals with SCD.

METHODS

This study introduces a novel integer ratio based 3D box-counting fractal analysis (IRBCFA) to quantify regional fractal dimensions (FDs) in structural magnetic resonance imaging (MRI) data. The innovative method overcomes limitations of conventional box-counting techniques by accommodating arbitrary box sizes, thereby enhancing the precision of FD estimation in small, yet neurologically significant, brain regions.

RESULTS

The application of IRBCFA to two publicly available datasets, OASIS-3 and ADNI, consisting of 520 and 180 subjects, respectively. The method identified discriminative regions of interest (ROIs) predominantly within the limbic system, fronto-parietal region, occipito-temporal region, and basal ganglia-thalamus region. These ROIs exhibited significant correlations with cognitive functions, including executive functioning, memory, social cognition, and sensory perception, suggesting their potential as neuroimaging markers for SCD. The identification model trained on these ROIs demonstrated exceptional performance achieving over 93 % accuracy on the discovery dataset and exceeding 87 % on the independent testing dataset. Furthermore, an exchange experiment between datasets revealed a substantial overlap in discriminative ROIs, highlighting the robustness of our method across diverse populations.

CONCLUSION

Our findings indicate that IRBCFA can serve as a valuable tool for quantifying the spatial complexity of gray matter, providing insights into cognitive aging and aiding in the automated identification of individuals with SCD. The demonstrated generalizability and robustness of this method position it as a promising tool for neurodegenerative disease research and offer potential for clinical applications.

Disrupted morphological brain network organization in subjective cognitive decline and mild cognitive impairment

We aim to investigate the alterations in gray matter for subjective cognitive decline (SCD) and mild cognitive impairment (MCI) from the perspective of the human connectome. High-resolution T1-weighted images were acquired from 54 patients with SCD, 95 patients with MCI, and 65 healthy controls (HC). Morphological brain networks (MBN) were constructed using similarities in the distribution of gray matter volumes between regions. The strength of morphological connections and topographic metrics derived from the graph-theoretical analysis were compared. Furthermore, we assessed the relationship between the observed morphological abnormalities and disease severity. According to the results, we found a significantly decreased morphological connection between the somatomotor network and ventral attention network in SCD compared to HC and MCI compared to SCD. The graph-theoretic analysis illustrated disruptions in the whole network organization, where the normalized shortest path increased and the global efficiency (Eg) decreased in MCI compared to SCD. In addition, Montreal Cognitive Assessment scores of SCD patients had a significantly negative correlation with Eg. The primary limitations of the present study include the cross-sectional design, no enrolled AD patients, no assessment of amyloidosis, and the need for more comprehensive neuropsychological tests. Our findings indicate the abnormalities of morphological networks at early stages in the AD continuum, which could be interpreted as compensatory changes to retain a normal level of cognitive function. The present study could provide new insight into the mechanism of AD.

Impact of exposure to natural versus urban soundscapes on brain functional connectivity, BOLD entropy and behavior

BACKGROUND

Humans have been moving from rural to urban environments for decades. This process may have important consequences for our health and well-being. Most previous studies have focused on visual input, and the auditory domain has been understudied so far. Therefore, we set out to investigate the influence of exposure to natural vs urban soundscapes on brain activity and behavior.

METHODS

Resting-state fMRI data was acquired while participants (N = 35) listened to natural and urban soundscapes. Two affective questionnaires (the Positive and Negative Affect Schedule (PANAS) and the Perceived Stress Scale) and two cognitive tasks (dual n-back (DNB) and the backward digit-span (BDS)) were assessed before and after each soundscape condition. To quantify brain function we used complexity and network measures, namely brain entropy (BEN) and whole brain functional connectivity (FC). To study the link between brain and behavior, changes in BEN and whole brain FC were correlated to changes in cognitive performance and self-reported affect.

RESULTS

We found higher BEN when listening to urban sounds in posterior cingulate gyrus, cuneus and precuneus, occipital lobe/calcarine as compared to nature sounds, which was negatively correlated to (post-pre) differences in positive affect (PANAS) in the urban soundscape condition. In addition, we found higher FC between areas in the auditory, cinguloopercular, somatomotor hand and mouth networks when listening to nature as compared to urban sounds which was positively correlated to (post-pre) differences of the of the composite score of Digit span and N-back for nature soundscape.

CONCLUSIONS

This study provides a framework for the neural underpinnings of how natural versus urban soundscapes affect both whole brain FC and BEN and bear implications for the understanding of how the physical auditory environment affects brain function and subsequently observed behavior. Moreover, correlations with cognition and affect reveal the meaning that exposure to soundscapes may have on the human brain. To the best of our knowledge this is the first study to analyze BEN and whole brain FC at rest during exposure to nature and urban soundscapes and to explore their relationship to behavior.

Does the Cognitive Change Index Predict Future Cognitive and Clinical Decline? Longitudinal Analysis in a Demographically Diverse Cohort

Background

The Cognitive Change Index (CCI) is a widely-used measure of self-perceived cognitive ability and change. Unfortunately, it is unclear if the CCI predicts future cognitive and clinical decline.

Objective

We evaluated baseline CCI to predict transition from normal cognition to cognitive impairment in nondemented older adults and in predementia groups including, subjective cognitive decline, motoric cognitive risk syndrome, and mild cognitive impairment. Different versions of the CCI were assessed to uncover any differential risk sensitivity. We also examined the effect of ethnicity/race on CCI.

Methods

Einstein Aging Study participants (N = 322, Mage = 77.57±4.96, % female=67.1, Meducation = 15.06±3.54, % non-Hispanic white = 46.3) completed an expanded 40-item CCI version (CCI-40) and neuropsychological evaluation (including Clinical Dementia Rating Scale [CDR], Montreal Cognitive Assessment, and Craft Story) at baseline and annual follow-up (Mfollow - up=3.4 years). CCI-40 includes the original 20 items (CCI-20) and the first 12 memory items (CCI-12). Linear mixed effects models (LME) and generalized LME assessed the association of CCI total scores at baseline with rate of decline in neuropsychological tests and CDR.

Results

In the overall sample and across predementia groups, the CCI was associated with rate of change in log odds on CDR, with higher CCI at baseline predicting faster increase in the odds of being impaired on CDR. The predictive validity of the CCI broadly held across versions (CCI-12, 20, 40) and ethnic/racial groups (non-Hispanic black and white).

Conclusions

Self-perception of cognitive change on the CCI is a useful marker of dementia risk in demographically/clinically diverse nondemented samples. All CCI versions successfully predicted decline.

Aberrant spontaneous static and dynamic amplitude of low-frequency fluctuations in cerebral small vessel disease with or without mild cognitive impairment

BACKGROUND

Cerebral small vessel disease (CSVD) is considered an age-related degenerative neurological disorder and the most common risk factor for vascular cognitive impairment (VCI). The amplitude of fluctuation of low frequency (ALFF) can detect altered intrinsic brain activity in CSVD. This study explored the static and dynamic ALFFs in the early stage of CSVD with (CSVD-M) or without (CSVD-W) mild cognitive impairment (MCI) in these patients and how these changes contribute to cognitive deterioration.

METHODS

Thirty consecutive CSVD cases and 18 healthy controls (HC) were included in this study. All the participants underwent a 3D magnetization-prepared rapid gradient-echo (MPRAGE) sequence to obtain structural T1-weighted images. Simultaneous multislice imaging 5(SMS5) was used for resting-state functional MRI (rs-fMRI), and Data Processing and Analysis of Brain Imaging software helped determine static ALFF (sALFF). The dynamic ALFF (dALFF) was calculated using the sliding window method of DynamicBC software. Analysis of Covariance (ANCOVA) and two-sample t-test were used to evaluate the sALFF and temporal variability of dALFF among the three groups. The subjects were rated on a broad standard neuropsychological scale. Partial correlation analysis was used to evaluate the correlation between sALFF and dALFF variability and cognition (Bonferroni correction, statistical threshold set at p < .05).

RESULTS

Compared with HCs, the CSVD-M group indicated decreased sALFF values in the bilateral cerebellum posterior lobe (CPL) and the left inferior Parietal Lobule (IPL), with increased sALFF values in the right SFG. For dALFF analysis, the CSVD-W group had significant dALFF variability in the right fusiform gyrus compared with HC. Moreover, the postcentral gyrus (PoCG) was significantly high in the CSVD-W group. While in the CSVD-M group, the bilateral paracentral lobules (PL) revealed significantly elevated dALFF variability and low dALFF variability in the left CPL and right IPL compared with HCs. The CSVD-M group had high dALFF variability in the bilateral PL but low dALFF variability in the left middle temporal gyrus (MTG) and right PoCG compared with the CSVD-W group. The partial correlation analysis indicated that dALFF variability in the left MTG was positively associated with EM (r = 0.713, p = .002) in CSVD-W and CSVD-M groups. In the groups with CSVD-M and HC, altered dALFF variability in the bilateral PL was negatively correlated with EM (r = -0.560, p = .002).

CONCLUSION

There were significant changes in sALFF and dALFF variability in CSVD patients. Abnormal spontaneous static and dynamic ALFFs may provide new insights into cognitive dysfunction in CSVD with MCI and may be valuable biomarkers for early diagnosis.