Brain activation disturbance for target detection in patients with mild cognitive impairment: an fMRI study

Functional and structural alterations of dorsal attention network in preclinical and early-stage Alzheimer's disease

OBJECTIVES

Subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) are known as the preclinical and early stage of Alzheimer's disease (AD). The dorsal attention network (DAN) is mainly responsible for the "top-down" attention process. However, previous studies mainly focused on single functional modality and limited structure. This study aimed to investigate the multimodal alterations of DAN in SCD and aMCI to assess their diagnostic value in preclinical and early-stage AD.

METHODS

Resting-state functional magnetic resonance imaging (MRI) was carried out to measure the fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), and functional connectivity (FC). Structural MRI was used to calculate the gray matter volume (GMV) and cortical thickness. Moreover, receiver-operating characteristic (ROC) analysis was used to distinguish these alterations in SCD and aMCI.

RESULTS

The SCD and aMCI groups showed both decreased ReHo in the right middle temporal gyrus (MTG) and decreased GMV compared to healthy controls (HCs). Especially in the SCD group, there were increased fALFF and increased ReHo in the left inferior occipital gyrus (IOG), decreased fALFF and increased FC in the left inferior parietal lobule (IPL), and reduced cortical thickness in the right inferior temporal gyrus (ITG). Furthermore, functional and structural alterations in the SCD and aMCI groups were closely related to episodic memory (EM), executive function (EF), and information processing speed (IPS). The combination of multiple indicators of DAN had a high accuracy in differentiating clinical stages.

CONCLUSIONS

Our current study demonstrated functional and structural alterations of DAN in SCD and aMCI, especially in the MTG, IPL, and SPL. Furthermore, cognitive performance was closely related to these significant alterations. Our study further suggested that the combined multiple indicators of DAN could be acted as the latent neuroimaging markers of preclinical and early-stage AD for their high diagnostic value.

An Activation Likelihood Estimation Meta-Analysis of Specific Functional Alterations in Dorsal Attention Network in Mild Cognitive Impairment

Background

Mild cognitive impairment (MCI) is known as the prodromal stage of the Alzheimer’s disease (AD) spectrum. The recent studies have advised that functional alterations in the dorsal attention network (DAN) could be used as a sensitive marker to forecast the progression from MCI to AD. Therefore, our aim was to investigate specific functional alterations in the DAN in MCI.

Methods

We systematically searched PubMed, EMBASE, and Web of Science and chose relevant articles based on the three functional indicators, the amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), and functional connectivity (FC) in the DAN in MCI. Based on the activation likelihood estimation, we accomplished the aggregation of specific coordinates and the analysis of functional alterations.

Results

A total of 38 studies were involved in our meta-analysis. By summing up included articles, we acquired specific brain region alterations in the DAN mainly in the superior temporal gyrus (STG), middle temporal gyrus (MTG), superior frontal gyrus (SFG), middle frontal gyrus (MFG), inferior frontal gyrus (IFG), precentral gyrus (preCG), inferior parietal lobule (IPL), superior parietal lobule (SPL). At the same time, the key area that shows anti-interaction with default mode network included the IPL in the DAN. The one showing interactions with executive control network was mainly in the MFG. Finally, the frontoparietal network showed a close connection with DAN especially in the IPL and IFG.

Conclusion

This study demonstrated abnormal functional markers in the DAN and its interactions with other networks in MCI group, respectively. It provided the foundation for future targeted interventions in preventing the progression of AD.

Systematic Review Registration

[https://www.crd.york.ac.uk/PROSPERO/], identifier [CRD42021287958].

Changes of Structural and Functional Attention Control Networks in Subclinical Hypothyroidism

Objective: This study aimed to explore the structural changes in patients with subclinical hypothyroidism (SCH) using voxel-based morphometry (VBM) and to investigate the altered attentional control networks using functional MRI (fMRI) during the performance of a modified Stroop task with Chinese characters. Methods: High-resolution three-dimensional (3D) T1-weighted images and an fMRI scan were taken from 18 patients with SCH and 18 matched control subjects. The Montreal Cognitive Assessment Chinese-revised (MoCA-CR) and the Stroop task were used to evaluate the cognitive and attention control of the participants. Results: Compared to controls, the VBM results showed decreased gray matter volumes (GMVs) in bilateral prefrontal cortices (PFCs, including middle, medial, and inferior frontal gyri), cingulate gyrus, precuneus, left middle temporal gyrus, and insula in patients with SCH. The fMRI results showed a distributed network of brain regions in both groups, consisting of PFCs (including superior and middle and inferior frontal cortices), anterior cingulate cortex (ACC), posterior cingulate cortex, and precuneus, as well as the insula and caudate nucleus. Compared to controls, the SCH group had lower activation of the above brain areas, especially during the color-naming task. In addition, the normalized GMV (nGMV) was negatively correlated with thyroid-stimulating hormone (TSH) level (r = -0.722, p < 0.001). Conclusion: Results indicate that patients with SCH exhibit reduced GMVs, altered BOLD signals, and activation in regions associated with attention control, which further suggest that patients with SCH may have attentional control deficiency, and the weakened PFC-ACC-precuneus brain network might be one of the neural mechanisms. Negative correlations between nGMV and TSH suggest that TSH elevation may induce abnormalities in the cortex.

Estimation of Brain Functional Connectivity in Patients with Mild Cognitive Impairment

Mild cognitive impairment is defined as greater cognitive decline than expected for a person at a particular age and is sometimes considered a stage between healthy aging and Alzheimer's disease or other dementia syndromes. It is known that functional connectivity patterns change in people with this diagnosis. We studied functional connectivity patterns and functional segregation in a resting-state fMRI paradigm comparing 10 MCI patients and 10 healthy controls matched by education level, age and sex. Ninety ROIs from the automated anatomical labeling (AAL) atlas were selected for functional connectivity analysis. A correlation matrix was created for each group, and a third matrix with the correlation coefficient differences between the two matrices was created. Functional segregation was analyzed with the 3-cycle method, which is novel in studies of this topic. Finally, cluster analyses were also performed. Our results showed that the two correlation matrices were visually similar but had many differences related to different cognitive functions. Differences were especially apparent in the anterior default mode network (DMN), while the visual resting-state network (RSN) showed no differences between groups. Differences in connectivity patterns in the anterior DMN should be studied more extensively to fully understand its role in the differentiation of healthy aging and an MCI diagnosis.

Functional MRI on executive functioning in aging and dementia: A scoping review of cognitive tasks

Cognitive decline with aging and dementia is especially poignant with regard to the executive functioning that is necessary for activities of daily independent living. The relationship between age-related neurodegeneration in the prefrontal cortex and executive functioning has been uniquely investigated using task-phase functional magnetic resonance imaging (fMRI) to detect brain activity in response to stimuli; however, a comprehensive list of task designs that have been implemented to task-phase fMRI is absent in the literature. The purpose of this review was to recognize what methods have been used to study executive functions with aging and dementia in fMRI tasks, and to describe and categorize them. The following cognitive subdomains were emphasized: cognitive flexibility, planning and decision-making, working memory, cognitive control/inhibition, semantic processing, attention and concentration, emotional functioning, and multitasking. Over 30 different task-phase fMRI designs were found to have been implemented in the literature, all adopted from standard neuropsychological assessments. Cognitive set-shifting and decision-making tasks were particularly well studied in regard to age-related neurodegeneration, while emotional functioning and multitasking designs were found to be the least utilized. Summarizing the information on which tasks have shown the greatest usability will assist in the future design and implementation of effective fMRI experiments targeting executive functioning.

Neurocognitive and Behavioral Indexes for Identifying the Amnestic Subtypes of Mild Cognitive Impairment

Early identification of amnestic mild cognitive impairment (aMCI) subtypes is important for early diagnosis and prognosis of Alzheimer’s disease. Healthy, single-domain (sdaMCI) and multiple-domain aMCI (mdaMCI) participants performed an auditory-visual distraction-attention task. Event-related brain potentials (ERPs) were recorded while the participants performed the task to evaluate Go/NoGo N2 and P3 ERP components. The results showed the expected behavioral and cognitive decline in mdaMCI participants relative to controls (fewer hits, longer reaction times [RTs], slightly smaller Go-N2 and NoGo-N2 amplitudes), while sdaMCI participants showed some decline (slightly longer RTs, smaller Go- and NoGo-N2 amplitudes) along with some unexpected results (a late positive slow wave, PSW) and good levels of execution. In addition, some of these parameters proved to be useful markers. Thus, the number of hits was the best marker for diagnosing mdaMCI participants (distinguishing them from controls, from sdaMCI participants, and from both groups together), while the PSW amplitude was the best marker for diagnosing sdaMCI participants (distinguishing them from controls, and from control & mdaMCI participants).

Working memory load-dependent spatio-temporal activity of single-trial P3 response detected with an adaptive wavelet denoiser

Working memory (WM) refers to the holding and manipulation of information during cognitive tasks. Its underlying neural mechanisms have been explored through both functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). Trial-by-trial coupling of simultaneously collected EEG and fMRI signals has become an important and promising approach to study the spatio-temporal dynamics of such cognitive processes. Previous studies have demonstrated a modulation effect of the WM load on both the BOLD response in certain brain areas and the amplitude of P3. However, much remains to be explored regarding the WM load-dependent relationship between the amplitude of ERP components and cortical activities, and the low signal-to-noise ratio (SNR) of the EEG signal still poses a challenge to performing single-trial analyses. In this paper, we investigated the spatio-temporal activities of P3 during an n-back verbal WM task by introducing an adaptive wavelet denoiser into the extraction of single-trial P3 features and using general linear model (GLM) to integrate simultaneously collected EEG and fMRI data. Our results replicated the modulation effect of the WM load on the P3 amplitude. Additionally, the activation of single-trial P3 amplitudes was detected in multiple brain regions, including the insula, the cuneus, the lingual gyrus (LG), and the middle occipital gyrus (MOG). Moreover, we found significant correlations between P3 features and behavioral performance. These findings suggest that the single-trial integration of simultaneous EEG and fMRI signals may provide new insights into classical cognitive functions.

Long-term cognitive impairment of breast cancer patients after chemotherapy: A functional MRI study

BACKGROUND

Chemotherapy, a prominent treatment for breast cancer (BC), can have detrimental side effects on the patient's cognitive functions including the executive function. However, the neurophysiological mechanism of the cognitive impairment remains unclear.

OBJECTIVE

The purpose of this study is to explore long-term chemotherapy-related functional connectivity changes using fMRI and the relationship between the connectivity changes and the executive function impairment in breast cancer patients.

METHODS

In this study, twenty-three breast cancer patients were treated with chemotherapy and twenty-six healthy subjects were recruited as the healthy control (HC) group. The functional connectivity of anterior cingulate cortex (ACC) was calculated from resting-state fMRI of the BC and control groups. The relationship between the functional connectivity of ACC and the executive function was further analyzed based on the patient' response time of the Stroop Interference Test.

RESULTS

The results show that functional connectivity of ACC in the BC group is significantly lower than that in the control group. The correlation analysis within the BC group indicates that the functional connectivity of ACC was significantly correlated with the executive function.

CONCLUSION

These findings provide evidence that the functional connectivity changes might be a pathophysiological basis for long-term chemotherapy-related cognitive dysfunction, along with executive function impairment in breast cancer patients.

Mild cognitive impairment and fMRI studies of brain functional connectivity: the state of the art

In the last 15 years, many articles have studied brain connectivity in Mild Cognitive Impairment patients with fMRI techniques, seemingly using different connectivity statistical models in each investigation to identify complex connectivity structures so as to recognize typical behavior in this type of patient. This diversity in statistical approaches may cause problems in results comparison. This paper seeks to describe how researchers approached the study of brain connectivity in MCI patients using fMRI techniques from 2002 to 2014. The focus is on the statistical analysis proposed by each research group in reference to the limitations and possibilities of those techniques to identify some recommendations to improve the study of functional connectivity. The included articles came from a search of Web of Science and PsycINFO using the following keywords: f MRI, MCI, and functional connectivity. Eighty-one papers were found, but two of them were discarded because of the lack of statistical analysis. Accordingly, 79 articles were included in this review. We summarized some parts of the articles, including the goal of every investigation, the cognitive paradigm and methods used, brain regions involved, use of ROI analysis and statistical analysis, emphasizing on the connectivity estimation model used in each investigation. The present analysis allowed us to confirm the remarkable variability of the statistical analysis methods found. Additionally, the study of brain connectivity in this type of population is not providing, at the moment, any significant information or results related to clinical aspects relevant for prediction and treatment. We propose to follow guidelines for publishing fMRI data that would be a good solution to the problem of study replication. The latter aspect could be important for future publications because a higher homogeneity would benefit the comparison between publications and the generalization of results.

A study of structural and functional connectivity in early Alzheimer's disease using rest fMRI and diffusion tensor imaging

BACKGROUND/OBJECTIVES

Alzheimer's disease (AD) is a progressive neurodegenerative condition where in early diagnosis and interventions are key policy priorities in dementia services and research. We studied the functional and structural connectivity in mild AD to determine the nature of connectivity changes that coexist with neurocognitive deficits in the early stages of AD.

METHODS

Fifteen mild AD subjects and 15 cognitively healthy controls (CHc) matched for age and gender, underwent detailed neurocognitive assessment and magnetic resonance imaging (MRI) of resting state functional MRI (rs-fMRI) and diffusion tensor imaging (DTI). Rest fMRI was analyzed using dual regression approach and DTI by voxel wise statistics.

RESULTS

Patients with mild AD had significantly lower functional connectivity (FC) within the default mode network and increased FC within the executive network. The mild AD group scored significantly lower in all domains of cognition compared with CHc. But fractional anisotropy did not significantly (p < 0.05) differ between the groups.

CONCLUSION

Resting state functional connectivity alterations are noted during initial stages of cognitive decline in AD, even when there are no significant white matter microstructural changes.

Toward systems neuroscience in mild cognitive impairment and Alzheimer's disease: a meta-analysis of 75 fMRI studies

Most of the previous task functional magnetic resonance imaging (fMRI) studies found abnormalities in distributed brain regions in mild cognitive impairment (MCI) and Alzheimer's disease (AD), and few studies investigated the brain network dysfunction from the system level. In this meta-analysis, we aimed to examine brain network dysfunction in MCI and AD. We systematically searched task-based fMRI studies in MCI and AD published between January 1990 and January 2014. Activation likelihood estimation meta-analyses were conducted to compare the significant group differences in brain activation, the significant voxels were overlaid onto seven referenced neuronal cortical networks derived from the resting-state fMRI data of 1,000 healthy participants. Thirty-nine task-based fMRI studies (697 MCI patients and 628 healthy controls) were included in MCI-related meta-analysis while 36 task-based fMRI studies (421 AD patients and 512 healthy controls) were included in AD-related meta-analysis. The meta-analytic results revealed that MCI and AD showed abnormal regional brain activation as well as large-scale brain networks. MCI patients showed hypoactivation in default, frontoparietal, and visual networks relative to healthy controls, whereas AD-related hypoactivation mainly located in visual, default, and ventral attention networks relative to healthy controls. Both MCI-related and AD-related hyperactivation fell in frontoparietal, ventral attention, default, and somatomotor networks relative to healthy controls. MCI and AD presented different pathological while shared similar compensatory large-scale networks in fulfilling the cognitive tasks. These system-level findings are helpful to link the fundamental declines of cognitive tasks to brain networks in MCI and AD.

Fine-granularity functional interaction signatures for characterization of brain conditions

In the human brain, functional activity occurs at multiple spatial scales. Current studies on functional brain networks and their alterations in brain diseases via resting-state functional magnetic resonance imaging (rs-fMRI) are generally either at local scale (regionally confined analysis and inter-regional functional connectivity analysis) or at global scale (graph theoretic analysis). In contrast, inferring functional interaction at fine-granularity sub-network scale has not been adequately explored yet. Here our hypothesis is that functional interaction measured at fine-granularity sub-network scale can provide new insight into the neural mechanisms of neurological and psychological conditions, thus offering complementary information for healthy and diseased population classification. In this paper, we derived fine-granularity functional interaction (FGFI) signatures in subjects with Mild Cognitive Impairment (MCI) and Schizophrenia by diffusion tensor imaging (DTI) and rs-fMRI, and used patient-control classification experiments to evaluate the distinctiveness of the derived FGFI features. Our experimental results have shown that the FGFI features alone can achieve comparable classification performance compared with the commonly used inter-regional connectivity features. However, the classification performance can be substantially improved when FGFI features and inter-regional connectivity features are integrated, suggesting the complementary information achieved from the FGFI signatures.