Impaired self-awareness of cognitive deficits in Parkinson's disease relates to cingulate cortex dysfunction

The cytoarchitectonic landscape revealed by deep learning method facilitated precise positioning in mouse neocortex

Neocortex is a complex structure with different cortical sublayers and regions. However, the precise positioning of cortical regions can be challenging due to the absence of distinct landmarks without special preparation. To address this challenge, we developed a cytoarchitectonic landmark identification pipeline. The fluorescence micro-optical sectioning tomography method was employed to image the whole mouse brain stained by general fluorescent nucleotide dye. A fast 3D convolution network was subsequently utilized to segment neuronal somas in entire neocortex. By approach, the cortical cytoarchitectonic profile and the neuronal morphology were analyzed in 3D, eliminating the influence of section angle. And the distribution maps were generated that visualized the number of neurons across diverse morphological types, revealing the cytoarchitectonic landscape which characterizes the landmarks of cortical regions, especially the typical signal pattern of barrel cortex. Furthermore, the cortical regions of various ages were aligned using the generated cytoarchitectonic landmarks suggesting the structural changes of barrel cortex during the aging process. Moreover, we observed the spatiotemporally gradient distributions of spindly neurons, concentrated in the deep layer of primary visual area, with their proportion decreased over time. These findings could improve structural understanding of neocortex, paving the way for further exploration with this method.

Combined brain topological metrics with machine learning to distinguish essential tremor and tremor-dominant Parkinson's disease

BACKGROUND

Essential tremor (ET) and Parkinson's disease (PD) are the two most prevalent movement disorders, sharing several overlapping tremor clinical features. Although growing evidence pointed out that changes in similar brain network nodes are associated with these two diseases, the brain network topological properties are still not very clear.

OBJECTIVE

The combination of graph theory analysis with machine learning (ML) algorithms provides a promising way to reveal the topological pathogenesis in ET and tremor-dominant PD (tPD).

METHODS

Topological metrics were extracted from Resting-state functional images of 86 ET patients, 86 tPD patients, and 86 age- and sex-matched healthy controls (HCs). Three steps were conducted to feature dimensionality reduction and four frequently used classifiers were adopted to discriminate ET, tPD, and HCs.

RESULTS

A support vector machine classifier achieved the best classification performance of four classifiers for discriminating ET, tPD, and HCs with 89.0% mean accuracy (mACC) and was used for binary classification. Particularly, the binary classification performances among ET vs. tPD, ET vs. HCs, and tPD vs. HCs were with 94.2% mACC, 86.0% mACC, and 86.3% mACC, respectively. The most power discriminative features were mainly located in the default, frontal-parietal, cingulo-opercular, sensorimotor, and cerebellum networks. Correlation analysis results showed that 2 topological features negatively and 1 positively correlated with clinical characteristics.

CONCLUSIONS

These results demonstrated that combining topological metrics with ML algorithms could not only achieve high classification accuracy for discrimination ET, tPD, and HCs but also help to reveal the potential brain topological network pathogenesis in ET and tPD.

Cognitive Fitness: Harnessing the Strength of Exerkines for Aging and Metabolic Challenges

Addressing cognitive impairment (CI) represents a significant global challenge in health and social care. Evidence suggests that aging and metabolic disorders increase the risk of CI, yet promisingly, physical exercise has been identified as a potential ameliorative factor. Specifically, there is a growing understanding that exercise-induced cognitive improvement may be mediated by molecules known as exerkines. This review delves into the potential impact of aging and metabolic disorders on CI, elucidating the mechanisms through which various exerkines may bolster cognitive function in this context. Additionally, the discussion extends to the role of exerkines in facilitating stem cell mobilization, offering a potential avenue for improving cognitive impairment.

Pathobiology of Cognitive Impairment in Parkinson Disease: Challenges and Outlooks

Cognitive impairment (CI) is a characteristic non-motor feature of Parkinson disease (PD) that poses a severe burden on the patients and caregivers, yet relatively little is known about its pathobiology. Cognitive deficits are evident throughout the course of PD, with around 25% of subtle cognitive decline and mild CI (MCI) at the time of diagnosis and up to 83% of patients developing dementia after 20 years. The heterogeneity of cognitive phenotypes suggests that a common neuropathological process, characterized by progressive degeneration of the dopaminergic striatonigral system and of many other neuronal systems, results not only in structural deficits but also extensive changes of functional neuronal network activities and neurotransmitter dysfunctions. Modern neuroimaging studies revealed multilocular cortical and subcortical atrophies and alterations in intrinsic neuronal connectivities. The decreased functional connectivity (FC) of the default mode network (DMN) in the bilateral prefrontal cortex is affected already before the development of clinical CI and in the absence of structural changes. Longitudinal cognitive decline is associated with frontostriatal and limbic affections, white matter microlesions and changes between multiple functional neuronal networks, including thalamo-insular, frontoparietal and attention networks, the cholinergic forebrain and the noradrenergic system. Superimposed Alzheimer-related (and other concomitant) pathologies due to interactions between α-synuclein, tau-protein and β-amyloid contribute to dementia pathogenesis in both PD and dementia with Lewy bodies (DLB). To further elucidate the interaction of the pathomechanisms responsible for CI in PD, well-designed longitudinal clinico-pathological studies are warranted that are supported by fluid and sophisticated imaging biomarkers as a basis for better early diagnosis and future disease-modifying therapies.

Bridging the Gap: Association between Objective and Subjective Outcomes of Communication Performance in People with Parkinson's Disease Evaluated for Deep Brain Stimulation

Background

Decrements in verbal fluency following deep brain stimulation (DBS) in people with Parkinson's disease (PwP) are common. As such, verbal fluency tasks are used in assessing DBS candidacy and target selection. However, the correspondence between testing performance and the patient's perception of communication abilities is not well-established.

Methods

The Communication Participation Item Bank (CPIB) was administered to 85 PwP during pre-DBS neuropsychological evaluations. Central tendencies for CPIB responses and correlations between CPIB total scores, clinical and demographic factors, and language-based tasks were examined.

Results

Most PwP indicated some degree of communication interference on the CPIB. Worse scores on semantic fluency and greater motor impairment were associated with more communication interference.

Conclusions

Our findings suggest an incomplete correspondence between commonly used language-based tests and patient-reported outcomes of communication abilities. The need for a functional communication instrument that reflects the different aspects of communication abilities in functional contexts is emphasized.

Association of Cortical Gyrification With Imaging and Serum Biomarkers in Patients With Parkinson Disease

BACKGROUND AND OBJECTIVES

Pathologic progression across the cortex is a key feature of Parkinson disease (PD). Cortical gyrification is a morphologic feature of human cerebral cortex that is tightly linked to the integrity of underlying axonal connectivity. Monitoring cortical gyrification reductions may provide a sensitive marker of progression through structural connectivity, preceding the progressive stages of PD pathology. We aimed to examine the progressive cortical gyrification reductions and their associations with overlying cortical thickness, white matter (WM) integrity, striatum dopamine availability, serum neurofilament light (NfL) chain, and CSF α-synuclein levels in PD.

METHODS

This study included a longitudinal dataset with baseline (T0), 1-year (T1), and 4-year (T4) follow-ups and 2 cross-sectional datasets. Local gyrification index (LGI) was computed from T1-weighted MRI data to measure cortical gyrification. Fractional anisotropy (FA) was computed from diffusion-weighted MRI data to measure WM integrity. Striatal binding ratio (SBR) was measured from 123Ioflupane SPECT scans. Serum NfL and CSF α-synuclein levels were also measured.

RESULTS

The longitudinal dataset included 113 patients with de novo PD and 55 healthy controls (HCs). The cross-sectional datasets included 116 patients with relatively more advanced PD and 85 HCs. Compared with HCs, patients with de novo PD showed accelerated LGI and FA reductions over 1-year period and a further decline at 4-year follow-up. Across the 3 time points, the LGI paralleled and correlated with FA (p = 0.002 at T0, p = 0.0214 at T1, and p = 0.0037 at T4) and SBR (p = 0.0095 at T0, p = 0.0035 at T1, and p = 0.0096 at T4) but not with overlying cortical thickness in patients with PD. Both LGI and FA correlated with serum NfL level (LGI: p < 0.0001 at T0, p = 0.0043 at T1; FA: p < 0.0001 at T0, p = 0.0001 at T1) but not with CSF α-synuclein level in patients with PD. In the 2 cross-sectional datasets, we revealed similar patterns of LGI and FA reductions and associations between LGI and FA in patients with more advanced PD.

DISCUSSION

We demonstrated progressive reductions in cortical gyrification that were robustly associated with WM microstructure, striatum dopamine availability, and serum NfL level in PD. Our findings may contribute biomarkers for PD progression and potential pathways for early interventions of PD.

Exploring impaired self-awareness of motor symptoms in Parkinson's disease: Resting-state fMRI correlates and the connection to mindfulness

OBJECTIVE

To further explore the phenomenon of impaired self-awareness of motor symptoms in patients with Parkinson's Disease by using an evaluated measurement approach applied in previous studies, while also examining its connection with dispositional mindfulness and possible correlates of functional connectivity.

BACKGROUND

Recently, the phenomenon of impaired self-awareness has been studied more intensively by applying different measurement and imaging methods. Existing literature also points towards a possible connection with mindfulness, which has not been examined in a cross-sectional study. There is no data available concerning correlates of functional connectivity.

METHODS

Non-demented patients with idiopathic Parkinson's Disease without severe depression were tested for impaired self-awareness for motor symptoms following a psychometrically evaluated approach. Mindfulness was measured by applying the German version of the Five Facet Mindfulness Questionnaire. A subset of eligible patients underwent functional MRI scanning. Spearman correlation analyses were performed to examine clinical data. Whole-brain voxelwise regressions between seed-based connectivity and behavioral measures were calculated to identify functional connectivity correlates of impaired self-awareness scores.

RESULTS

A total of 41 patients with Parkinson's Disease were included. 15 patients successfully underwent resting-state fMRI scanning. Up to 88% of patients showed signs of impaired self-awareness. Awareness for hypokinetic movements correlated with total mindfulness values and three facets, while awareness for dyskinetic movements did not. Three significant clusters between scores of impaired self-awareness in general and for dyskinetic movements were identified linking behavioral measures with the functional connectivity of the inferior frontal gyrus, the right insular cortex, the supplementary motor area, and the precentral gyrus among others. Impaired self-awareness for hypokinetic movements did not have any neural correlate.

CONCLUSIONS

Clinical data is comparable with results from previous studies applying the same structured approach to measure impaired self-awareness in Parkinson's Disease. Functional connectivity analyses were conducted for the first time to evaluate neural correlates thereof. This data does not support a connection between impaired self-awareness of motor symptoms and dispositional mindfulness.