Apathy is associated with white matter network disruption and specific cognitive deficits in Parkinson's disease

Predicting Alzheimer's progression in MCI: a DTI-based white matter network model

OBJECTIVE

This study aimed to identify features of white matter network attributes based on diffusion tensor imaging (DTI) that might lead to progression from mild cognitive impairment (MCI) and construct a comprehensive model based on these features for predicting the population at high risk of progression to Alzheimer's disease (AD) in MCI patients.

METHODS

This study enrolled 121 MCI patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Among them, 36 progressed to AD after four years of follow-up. A brain network was constructed for each patient based on white matter fiber tracts, and network attribute features were extracted. White matter network features were downscaled, and white matter markers were constructed using an integrated downscaling approach, followed by forming an integrated model with clinical features and performance evaluation.

RESULTS

APOE4 and ADAS scores were used as independent predictors and combined with white matter network markers to construct a comprehensive model. The diagnostic efficacy of the comprehensive model was 0.924 and 0.919, sensitivity was 0.864 and 0.900, and specificity was 0.871 and 0.815 in the training and test groups, respectively. The Delong test showed significant differences (P < 0.05) in the diagnostic efficacy of the combined model and APOE4 and ADAS scores, while there was no significant difference (P > 0.05) between the combined model and white matter network biomarkers.

CONCLUSIONS

A comprehensive model constructed based on white matter network markers can identify MCI patients at high risk of progression to AD and provide an adjunct biomarker helpful in early AD detection.

Topological alteration of the brain structural network in Parkinson's disease with apathy

BACKGROUND

Apathy is a common neuropsychiatric manifestations in Parkinson's disease (PD), but neural network mechanisms still remain elusive. We aim to investigate the topological alteration of the brain structural network in PD with apathy.

METHOD

In the present study, a total of 47 apathetic PD (aPD) patients, 37 non-apathetic PD (naPD) patients, and 40 healthy controls (HCs) were enrolled. Diffusion tensor imaging (DTI) in conjunction with graph-theoretic approaches were used to explore the alterations of topological properties of the WM structural network arising from apathy in PD. One-way analysis of covariance and post hoc analyses were performed to explore differences among the three groups. Correlations were ascertained to examine relationships between the Starkstein Apathy Scale (AS) scores and significantly different network metrics among the three groups.

RESULTS

Both aPD and naPD patients remained small-world topology. However, compared with the naPD patients, aPD patients showed increased clustering coefficient (Cp) at the global level. At the regional level, aPD exhibited decreased nodal properties, mainly in the right dorsolateral prefrontal cortex (DLPFC), the right caudate nucleus (CAU), the right hippocampus, and the right superior parietal gyrus. Further, AS scores were negatively correlated with degree centrality of the right DLPFC (r = -0.254, p = 0.020) and the right CAU ( r = -0.357, p = 0.001) in the pooled patients with PD.

CONCLUSIONS

The findings suggested that apathy in PD presented relatively optimized global topological properties of the brain structural network and disrupted topological organization of the regional network, particularly involving the fronto-striatal-limbic circuits. The altered topological properties of abnormal brain regions might be used to understand the physiopathologic mechanism of the neural network in aPD patients.

Global Alterations of Whole Brain Structural Connectome in Parkinson's Disease: A Meta-analysis

Recent graph-theoretical studies of Parkinson's disease (PD) have examined alterations in the global properties of the brain structural connectome; however, reported alterations are not consistent. The present study aimed to identify the most robust global metric alterations in PD via a meta-analysis. A comprehensive literature search was conducted for all available diffusion MRI structural connectome studies that compared global graph metrics between PD patients and healthy controls (HC). Hedges' g effect sizes were calculated for each study and then pooled using a random-effects model in Comprehensive Meta-Analysis software, and the effects of potential moderator variables were tested. A total of 22 studies met the inclusion criteria for review. Of these, 16 studies reporting 10 global graph metrics (916 PD patients; 560 HC) were included in the meta-analysis. In the structural connectome of PD patients compared with HC, we found a significant decrease in clustering coefficient (g = -0.357, P = 0.005) and global efficiency (g = -0.359, P < 0.001), and a significant increase in characteristic path length (g = 0.250, P = 0.006). Dopaminergic medication, sex and age of patients were potential moderators of global brain network changes in PD. These findings provide evidence of decreased global segregation and integration of the structural connectome in PD, indicating a shift from a balanced small-world network to 'weaker small-worldization', which may provide useful markers of the pathophysiological mechanisms underlying PD.

Research Progress in Diffusion Spectrum Imaging

Studies have demonstrated that many regions in the human brain include multidirectional fiber tracts, in which the diffusion of water molecules within image voxels does not follow a Gaussian distribution. Therefore, the conventional diffusion tensor imaging (DTI) that hypothesizes a single fiber orientation within a voxel is intrinsically incapable of revealing the complex microstructures of brain tissues. Diffusion spectrum imaging (DSI) employs a pulse sequence with different b-values along multiple gradient directions to sample the diffusion information of water molecules in the entire q-space and then quantitatively estimates the diffusion profile using a probability density function with a high angular resolution. Studies have suggested that DSI can reliably observe the multidirectional fibers within each voxel and allow fiber tracking along different directions, which can improve fiber reconstruction reflecting the true but complicated brain structures that were not observed in the previous DTI studies. Moreover, with increasing angular resolution, DSI is able to reveal new neuroimaging biomarkers used for disease diagnosis and the prediction of disorder progression. However, so far, this method has not been used widely in clinical studies, due to its overly long scanning time and difficult post-processing. Within this context, the current paper aims to conduct a comprehensive review of DSI research, including the fundamental principles, methodology, and application progress of DSI tractography. By summarizing the DSI studies in recent years, we propose potential solutions towards the existing problem in the methodology and applications of DSI technology as follows: (1) using compressed sensing to undersample data and to reconstruct the diffusion signal may be an efficient and promising method for reducing scanning time; (2) the probability density function includes more information than the orientation distribution function, and it should be extended in application studies; and (3) large-sample study is encouraged to confirm the reliability and reproducibility of findings in clinical diseases. These findings may help deepen the understanding of the DSI method and promote its development in clinical applications.

Apathy in Parkinson's Disease: Defining the Park Apathy Subtype

Apathy is a neurobehavioural symptom affecting Parkinson's disease patients of all disease stages. Apathy seems to be associated with a specific underlying non-motor disease subtype and reflects dysfunction of separate neural networks with distinct neurotransmitter systems. Due to the complicated neuropsychiatric aetiology of apathy, clinical assessment of this invalidating non-motor symptom remains challenging. We aim to summarize the current findings on apathy in Parkinson's disease and highlight knowledge gaps. We will discuss the prevalence rates across the different disease stages and suggest screening tools for clinically relevant apathetic symptoms. We will approach the fundamental knowledge on the neural networks implicated in apathy in a practical manner and formulate recommendations on patient-tailored treatment. We will discuss the Park apathy phenotype in detail, shedding light on different clinical manifestations and implications for prognosis. With this review, we strive to distil the vast available theoretical knowledge into a clinical and patient-oriented perspective.

Apathy as a Within-Person Mediator of Depressive Symptoms and Cognition in Parkinson's Disease: Longitudinal Mediation Analyses

OBJECTIVE

Greater depressive symptoms are associated with worse cognitive functions in Parkinson's disease (PD); however, it is unclear what underlying factors drive this association. Apathy commonly develops in PD and may be a pathway through which depressive symptoms negatively influence cognition. Prior research examining depressive symptoms, apathy, and cognition in PD is limited by being predominantly cross-sectional. This study examined the role of apathy as a within- and between-person mediator for the longitudinal relationships between depression severity and cognitive functioning in patients with early PD.

METHODS

Participants included 487 individuals newly diagnosed with PD followed annually for up to 5 years by the Parkinson's Progression Marker Initiative. At each visit, participants completed depressive symptom measures, apathy ratings, and cognitive tests. Multi-level structural equation models examined both the within- and between-person effects of depressive symptoms on cognition through apathy, controlling for demographics and motor severity.

RESULTS

At the within-person level, apathy mediated the association between depressive symptoms and select cognitive functions (global cognition, attention/working memory, visuospatial functions, and immediate verbal memory; indirect effects, bootstrap p's <0.05). Significant between-person direct effects were found for depressive symptoms predicting apathy (boostrap p <0.001) and lower scores on most cognitive tests (bootstrap p's <0.05). However, the indirect effects did not reach significance, suggesting between-person mediation did not occur.

CONCLUSION

Findings suggest worsening of depressive symptoms over time in patients with PD may be a risk factor for increased apathy and subsequent decline in specific cognitive functions.

The neuropsychiatry of Parkinson's disease: advances and challenges

In people with Parkinson's disease, neuropsychiatric signs and symptoms are common throughout the disease course. These symptoms can be disabling and as clinically relevant as motor symptoms, and their presentation can be similar to, or distinct from, their counterparts in the general population. Correlates and risk factors for developing neuropsychiatric signs and symptoms include demographic, clinical, and psychosocial characteristics. The underlying neurobiology of these presentations is complex and not well understood, with the strongest evidence for neuropathological changes associated with Parkinson's disease, mechanisms linked to dopaminergic therapy, and effects not specific to Parkinson's disease. Assessment instruments and formal diagnostic criteria exist, but there is little routine screening of these signs and symptoms in clinical practice. Mounting evidence supports a range of pharmacological and non-pharmacological interventions, but relatively few efficacious treatment options exist. Optimising the management of neuropsychiatric presentations in people with Parkinson's disease will require additional research, raised awareness, specialised training, and development of innovative models of care.

Apathy and neurocognitive correlates: review from the perspective of 'precision psychiatry'

PURPOSE OF REVIEW

From the perspective of motivated behaviour and the so-called 'precision psychiatry', we try to identify recent advances in the neurocognitive and biological correlates of apathy.

RECENT FINDINGS

New evidence supports the notion that apathy is a common transdiagnostic and heterogeneous clinical syndrome, now conceptualized as a reduction in 'goal-directed' activity. Similarly, abundant evidence has been found related to neurocognitive correlates of apathy and the associations between clinical apathy and the processes primarily responsible for mediating motivational drive and effort-based decision making.Notwithstanding that the neurobiological basis is still poorly understood, there is some agreement in recent articles about a common system-level mechanism underlying apathy, pointing at specific medial frontal cortex and subcortical structures, including anterior cingulate cortex, medial orbitofrontal cortex and ventral striatum and related circuitry.

SUMMARY

Although difficulties in interpreting the results of these studies are apparent, because of different concepts of apathy used and methodological shortcomings identified, we have found consistent advances in the neurocognitive and biological correlates of apathy, relevant for the deep phenotyping proposed by the 'precision psychiatry' approach. This framework may eventually facilitate the identification of predictive-risk models and new specific therapeutic targets in psychiatry.