Structural network efficiency predicts cognitive decline in cerebral small vessel disease

Recurrent endothelin-1 mediated vascular insult leads to cognitive impairment protected by trophic factor pleiotrophin

Vascular dementia (VaD) is a complex neurodegenerative condition, with cerebral small vessel dysfunctions as the central role in its pathogenesis. Given the lack of suitable animal models to study the disease pathogenesis, we developed a mouse model to closely emulate the clinical scenarios of recurrent transient ischemic attacks (TIAs) leading to VaD using vasoconstricting peptide Endothelin-1(ET-1). We observed that administration of ET-1 led to blood-brain barrier (BBB) disruption and detrimental changes in its components, such as endothelial cells and pericytes, along with neuronal loss and synaptic dysfunction, resulting in irreversible memory loss. Further, in our pursuit of understanding potential interventions, we co-administered pleiotrophin (PTN) alongside ET-1 injections. PTN exhibited remarkable efficacy in preserving vital components of the BBB, including endothelial cells and pericytes, thereby restoring BBB integrity, preventing neuronal loss, and enhancing memory function. Our findings give a valuable framework for understanding the detrimental effects of multiple TIAs on brain health and provide a useful animal model to explore VaD's underlying mechanisms further and pave the way for promising therapies.

Distributed associations among white matter hyperintensities and structural brain networks with fluid cognition in healthy aging

White matter hyperintensities (WMHs) are associated with age-related cognitive impairment and increased risk of Alzheimer's disease. However, the manner by which WMHs contribute to cognitive impairment is unclear. Using a combination of predictive modeling and network neuroscience, we investigated the relationship between structural white matter connectivity and age, fluid cognition, and WMHs in 68 healthy adults (18-78 years). Consistent with previous work, WMHs were increased in older adults and exhibited a strong negative association with fluid cognition. Extending previous work, using predictive modeling, we demonstrated that age, WMHs, and fluid cognition were jointly associated with widespread alterations in structural connectivity. Subcortical-cortical connections between the thalamus/basal ganglia and frontal and parietal regions of the default mode and frontoparietal networks were most prominent. At the network level, both age and WMHs were negatively associated with network density and communicability, and positively associated with modularity. Spatially, WMHs were most prominent in arterial zones served by the middle cerebral artery and associated lenticulostriate branches that supply subcortical regions. Finally, WMHs overlapped with all major white matter tracts, most prominently in tracts that facilitate subcortical-cortical communication and are implicated in fluid cognition, including the anterior thalamic-radiations and forceps minor. Finally, results of mediation analyses suggest that whole-brain WMH load influences age-related decline in fluid cognition. Thus, across multiple levels of analysis, we showed that WMHs were increased in older adults and associated with altered structural white matter connectivity and network topology involving subcortical-cortical pathways critical for fluid cognition.

Are we ready to cure post-stroke cognitive impairment? Many key prerequisites can be achieved quickly and easily

PURPOSE

Post-stroke (PS) cognitive impairment (CI) is frequent and its devastating functional and vital consequences are well known. Despite recent guidelines, they are still largely neglected. A large number of recent studies have re-examined the epidemiology, diagnosis, imaging determinants and management of PSCI. The aim of this update is to determine whether these new data answer the questions that are essential to reducing PSCI, the unmet needs, and steps still to be taken.

METHODS

Literature review of stroke unit-era studies examining key steps in the management of PSCI: epidemiology and risk factors, diagnosis (cognitive profile and assessments), imaging determinants (quantitative measures, voxelwise localization, the disconnectome and associated Alzheimer's disease [AD]) and treatment (secondary prevention, symptomatic drugs, rehabilitation and noninvasive brain stimulation) of PSCI.

FINDINGS

(1) the prevalence of PSCI of approximately 50% is probably underestimated; (2) the sensitivity of screening tests should be improved to detect mild PSCI; (3) comprehensive assessment is now well-defined and should include apathy; (4) easily available factors can identify patients at high risk of PSCI; (5) key imaging determinants are the location and volume of the lesion and the resulting disconnection, associated AD and brain atrophy; WMH, ePVS, microhemorrhages, hemosiderosis, and cortical microinfarcts may contribute to cognitive impairment but are more likely to be markers of brain vulnerability or associated AD that reduce PS recovery; (6) remote and online assessment is a promising approach for selected patients; (7) secondary stroke prevention has not been proven to prevent PSCI; (8) symptomatic drugs are ineffective in treating PSCI and apathy; (9) in addition to cognitive rehabilitation, the benefits of training platforms and computerized training are yet to be documented; (10) the results and the magnitude of improvement of noninvasive brain stimulation, while very promising, need to be substantiated by large, high-quality, sham-controlled RCTs.

DISCUSSION AND CONCLUSION

These major advances pave the way for the reduction of PSCI. They include (1) the development of more sensitive screening tests applicable to all patients and (2) online remote assessment; crossvalidation of (3) clinical and (4) imaging factors to (5) identify patients at risk, as well as (6) factors that prompt a search for associated AD; (7) the inclusion of cognitive outcome as a secondary endpoint in acute and secondary stroke prevention trials; and (8) the validation of the benefit of noninvasive brain stimulation through high-quality, randomized, sham-controlled trials. Many of these objectives can be rapidly and easily attained.

Disrupted brain structural networks associated with depression and cognitive dysfunction in cerebral small vessel disease with microbleeds

Emerging evidence highlights cerebral microbleeds (CMBs) as hallmarks of cerebral small vessel disease (CSVD) underlying depression and cognitive dysfunction. This study aimed to reveal how depression and cognition-related white matter (WM) abnormalities are topologically presented, and the network-level structural disruptions associated with CMBs in CSVD. We used probabilistic diffusion tractography and graph theory to investigate brain WM network topology in CSVD patients with (n = 64, CSVD-c) and without (n = 138, CSVD-n) CMBs and 90 healthy controls. Then we evaluated the Pearson's correlations between disrupted network metrics and neuropsychological parameters. For global topology, the CSVD-c group exhibited significantly decreased global (Eglob) and local (Eloc) efficiency and increased shortest path length compared with the controls, while no significant difference was found between the CSVD-c and CSVD-n groups. For regional topology, although all groups showed highly similar hub distributions, compare with control group, the CSVD-c group exhibited significantly decreased nodal efficiency mainly in the bilateral supplementary motor area (SMA), median cingulate gyrus (DCG) and right orbital middle frontal gyrus, while the CSVD-n group showed significantly decreased nodal efficiency only in the right SMA. Notably, Eglob, Eloc and nodal efficiency of the right anterior cingulate gyrus, DCG, middle temporal gyrus and left insula showed significantly negative correlations with depression score, significantly positive correlations with Rey auditory verbal learning test and symbol digit modalities test scores in CSVD-n group, as well as significantly negative correlations with Stroop color-word test scores in CSVD-c group. The WM networks of CSVD patients are characterized by decreased global integration and local specialization, and decreased nodal efficiency highly related to depression and cognitive dysfunction in the attention, default mode network and sensorimotor regions. These findings provide new insight into the neurobiological mechanisms of CSVD and concomitant affective and cognitive disorders.

Long-range connections damage in white matter hyperintensities affects information processing speed

White matter hyperintensities, one of the major markers of cerebral small vessel disease, disrupt the integrity of neuronal networks and ultimately contribute to cognitive dysfunction. However, a deeper understanding of how white matter hyperintensities related to the connectivity patterns of brain hubs at the neural network level could provide valuable insights into the relationship between white matter hyperintensities and cognitive dysfunction. A total of 36 patients with moderate to severe white matter hyperintensities (Fazekas score ≥ 3) and 34 healthy controls underwent comprehensive neuropsychological assessments and resting-state functional MRI scans. The voxel-based graph-theory approach-functional connectivity strength was employed to systematically investigate the topological organization of the whole-brain networks. The white matter hyperintensities patients performed significantly worse than the healthy controls in episodic memory, executive function and information processing speed. Additionally, we found that white matter hyperintensities selectively affected highly connected hub regions, predominantly involving the medial and lateral prefrontal, precuneus, inferior parietal lobule, insula and thalamus. Intriguingly, this impairment was connectivity distance-dependent, with the most prominent disruptions observed in long-range connections (e.g. 100-150 mm). Finally, these disruptions of hub connectivity (e.g. the long-range functional connectivity strength in the left dorsolateral prefrontal cortex) positively correlated with the cognitive performance in white matter hyperintensities patients. Our findings emphasize that the disrupted hub connectivity patterns in white matter hyperintensities are dependent on connection distance, especially longer-distance connections, which in turn predispose white matter hyperintensities patients to worse cognitive function.

Structural Network Efficiency Predicts Conversion to Incident Parkinsonism in Patients With Cerebral Small Vessel Disease

BACKGROUND

To investigate whether structural network disconnectivity is associated with parkinsonian signs and their progression, as well as with an increased risk of incident parkinsonism.

METHODS

In a prospective cohort (Radboud University Nijmegen Diffusion Tensor and Magnetic Resonance Cohort study) consisting of 293 participants with small vessel disease (SVD), we assessed parkinsonian signs and incident parkinsonism over an 8-year follow-up. In addition, we reconstructed the white matter network followed by graph-theoretical analyses to compute the network metrics. Conventional magnetic resonance imaging markers for SVD were assessed.

RESULTS

We included 293 patients free of parkinsonism at baseline (2011), with a mean age 68.8 (standard deviation [SD] 8.4) years, and 130 (44.4%) were men. Nineteen participants (6.5%) developed parkinsonism during a median (SD) follow-up time of 8.3 years. Compared with participants without parkinsonism, those with all-cause parkinsonism had higher Unified Parkinson's Disease Rating scale (UPDRS) scores and lower global efficiency at baseline. Baseline global efficiency was associated with UPDRS motor scores in 2011 (β = -0.047, p < .001) and 2015 (β = -0.84, p < .001), as well as with the changes in UPDRS scores during the 4-year follow-up (β = -0.63, p = .004). In addition, at the regional level, we identified an inter-hemispheric disconnected network associated with an increased UPDRS motor score. Besides, lower global efficiency was associated with an increased risk of all-cause and vascular parkinsonism independent of SVD markers.

CONCLUSIONS

Our findings suggest that global network efficiency is associated with a gradual decline in motor performance, ultimately leading to incident parkinsonism in the elderly with SVD. Global network efficiency may have the added value to serve as a useful marker to capture changes in motor signs.

Altered dynamic functional network connectivity and topological organization variance in patients with white matter hyperintensities

White matter hyperintensities (WMHs) of presumed vascular origin are important imaging biomarkers of cerebral small vessel disease (CSVD). Previous studies have verified abnormal functional brain networks in CSVD. However, most of these studies rely on static functional connectivity, and only a few focus on the varying severity of the WMHs. Hence, our study primarily explored the disrupted dynamic functional network connectivity (dFNC) and topological organization variance in patients with WMHs. This study included 38 patients with moderate WMHs, 47 with severe WMHs, and 68 healthy controls (HCs). Ten independent components were chosen using independent component analysis based on resting-state functional magnetic resonance imaging. The dFNC of each participant was estimated using sliding windows and k-means clustering. We identified three reproducible dFNC states. Among them, patients with WMHs had a significantly higher occurrence in the sparsely connected State 1, but a lower occurrence and shorter duration in the positive and stronger connected State 3. Regarding topological organization variance, patients with WMHs showed higher variance in local efficiency but not global efficiency compared to HCs. Among the WMH subgroups, patients with severe WMHs showed similar but more obvious alterations than those with moderate WMHs. These altered network characteristics indicated an imbalance between the functional segregation and integration of brain networks, which was correlated with global cognition, memory, executive functions, and visuospatial abilities. Our study confirmed aberrant dFNC state metrics and topological organization variance in patients with moderate-to-severe WMHs; thus, it might provide a new pathway for exploring the pathogenesis of cognitive impairment.

The effects of CNS atrophy and ICVD on tests of executive function and functional status are mediated by intelligence

Background

Impairments in executive function (EF) are often attributed to ischemic cerebrovascular disease (ICVD) and frontal circuit pathology. However, EF can be distinguished from general intelligence and the latter is likely to manifest in "executive" measures. We aimed to distinguish the effects of imaging biomarkers on these constructs.

Methods

We tested neuroimaging biomarkers as independent predictors of observed 12 month-prospective cognitive performance by a Multiple Indicators Multiple Causes (MIMIC) model in the Alzheimer's Disease Neuroimaging Initiative (ADNI) (N ≅ 1750).

Results

ICVD was associated with ''Organization" (ORG) and "Planning" (PLAN) domain scores from the test of Every Day Cognition. Left anterior cingulate (LAC) atrophy was independently associated with Trail-Making part B and Animal Naming. The MIMIC model had excellent fit and tests additional latent variables i.e., EF and dEF (a latent δ homolog derived from Spearman's general intelligence factor, g). Only dEF was associated with instrumental activities of daily living (IADL). ICVD and LAC were both associated with observed executive measures through dEF. ICVD was independently associated with those same measures through EF.

Conclusions

Observed EF is independently determined by multiple factors. The effects of EF-associated MRI biomarkers can be related to disability and dementia only via their effects on g. Because g /δ are unlikely to be located within the frontal lobes, the dementia-specific variance in executive measures may have little to do with either frontal structure or function. Conversely, domain-specific variance in EF may have little to do with either IADL-impairment or dementia.

Mining Correlation between Fluid Intelligence and Whole-brain Large Scale Structural Connectivity

Exploring the neural basis of intelligence and the corresponding associations with brain network has been an active area of research in network neuroscience. Up to now, the majority of explorations mining human intelligence in brain connectomics leverages whole-brain functional connectivity patterns. In this study, structural connectivity patterns are instead used to explore relationships between brain connectivity and different behavioral/cognitive measures such as fluid intelligence. Specifically, we conduct a study using the 397 unrelated subjects from Human Connectome Project (Young Adults) dataset to estimate individual level structural connectivity matrices. We show that topological features, as quantified by our proposed measurements: Average Persistence (AP) and Persistent Entropy (PE), has statistically significant associations with different behavioral/cognitive measures. We also perform a parallel study using traditional graph-theoretical measures, provided by Brain Connectivity Toolbox, as benchmarks for our study. Our findings indicate that individual's structural connectivity indeed offers reliable predictive power of different behavioral/cognitive measures, including but not limited to fluid intelligence. Our results suggest that structural connectomes provide complementary insights (compared to using functional connectomes) in predicting human intelligence and warrants future studies on human intelligence and/or other behavioral/cognitive measures involving multi-modal approach.

Reproducibility of regional structural and functional MRI networks in cerebral small vessel disease compared to age matched and stroke-free controls

Abnormalities in structural and functional MRI connectivity measures have been reported in cerebral small vessel disease (SVD). Previous research has shown that whole-brain structural connectivity was highly reproducible in SVD patients, while whole-brain functional connectivity showed low reproducibility. It remains unclear whether the lower reproducibility of functional networks reported in SVD is due to selective disruption of reproducibility in specific networks or is generalised in patients with SVD. In this case-control study 15 SVD and 10 age-matched control participants were imaged twice with diffusion tensor imaging and resting state fMRI. Structural and functional connectivity matrices were constructed from this data and the default mode, fronto-parietal, limbic, salience, somatomotor and visual networks were extracted and the average connectivity between connections calculated and used to determine their reproducibility. Regional structural networks were more reproducible than functional networks, all structural networks showed ICC values ≥0.64 (except the salience network in SVD). The functional networks showed greater reproducibility in the controls compared to SVD with ICC values >0.7 for control participants and <=0.5 for the SVD group. The default mode network showed the greatest reproducibility for both control and SVD groups. Reproducibility of functional networks was affected by disease status with lower reproducibility in SVD compared with controls.

Superficial white matter microstructure affects processing speed in cerebral small vessel disease

White matter hyperintensities (WMH) are a typical feature of cerebral small vessel disease (CSVD), which contributes to about 50% of dementias worldwide. Microstructural alterations in deep white matter (DWM) have been widely examined in CSVD. However, little is known about abnormalities in superficial white matter (SWM) and their relevance for processing speed, the main cognitive deficit in CSVD. In 141 CSVD patients, processing speed was assessed using Trail Making Test Part A. White matter abnormalities were assessed by WMH burden (volume on T2-FLAIR) and diffusion MRI measures. SWM imaging measures had a large contribution to processing speed, despite a relatively low SWM WMH burden. Across all imaging measures, SWM free water (FW) had the strongest association with processing speed, followed by SWM mean diffusivity (MD). SWM FW was the only marker to significantly increase between two subgroups with the lowest WMH burdens. When comparing two subgroups with the highest WMH burdens, the involvement of WMH in the SWM was accompanied by significant differences in processing speed and white matter microstructure. Mediation analysis revealed that SWM FW fully mediated the association between WMH volume and processing speed, while no mediation effect of MD or DWM FW was observed. Overall, results suggest that the SWM has an important contribution to processing speed, while SWM FW is a sensitive imaging marker associated with cognition in CSVD. This study extends the current understanding of CSVD-related dysfunction and suggests that the SWM, as an understudied region, can be a potential target for monitoring pathophysiological processes.

Structural network efficiency mediates the association between glymphatic function and cognition in mild VCI: a DTI-ALPS study

Background and objective: Vascular cognitive impairment (VCI) can be caused by multiple types of cerebrovascular pathology and is considered a network disconnection disorder. The heterogeneity hinders research progress in VCI. Glymphatic failure has been considered as a key common pathway to dementia recently. The emergence of a new method, Diffusion Tensor Image Analysis Along the Perivascular Space (DTI-ALPS), makes it possible to investigate the changes of the glymphatic function in humans non-invasively. We aimed to investigate alterations of glymphatic function in VCI and its potential impact on network connectivity. Methods: We recruited 79 patients with mild VCI, including 40 with cerebral small vessel disease cognitive impairment (SVCI) and 39 with post-stroke cognitive impairment (PSCI); and, 77 normal cognitive (NC) subjects were recruited. All subjects received neuropsychological assessments and multimodal magnetic resonance imaging scans. ALPS-index was calculated and structural networks were constructed by deterministic tractography, and then, the topological metrics of these structural connectivity were evaluated. Results: The ALPS-index of VCI patients was significantly lower than that of NC subjects (P < 0.001). Multiple linear regression analysis showed that ALPS-index affects cognitive function independently (β = 0.411, P < 0.001). The results of correlation analysis showed that the ALPS-index was correlated with overall vascular risk factor burden (r = -0.263, P = 0.001) and multiple cerebrovascular pathologies (P < 0.05). In addition, global efficiency (Eg) of network was correlated with ALPS-index in both SVCI (r = 0.348, P = 0.028) and PSCI (r = 0.732, P < 0.001) patients. Finally, the results of mediation analysis showed that Eg partially mediated in the impact of glymphatic dysfunction on cognitive impairment (indirect effect = 7.46, 95% CI 4.08-11.48). Conclusion: In both major subtypes of VCI, the ALPS-index was decreased, indicating impaired glymphatic function in VCI. Glymphatic dysfunction may affect cognitive function in VCI by disrupting network connectivity, and, may be a potential common pathological mechanism of VCI. ALPS-index is expected to become an emerging imaging marker for VCI.

Multimodal tract-based MRI metrics outperform whole brain markers in determining cognitive impact of small vessel disease-related brain injury

In cerebral small vessel disease (cSVD), whole brain MRI markers of cSVD-related brain injury explain limited variance to support individualized prediction. Here, we investigate whether considering abnormalities in brain tracts by integrating multimodal metrics from diffusion MRI (dMRI) and structural MRI (sMRI), can better capture cognitive performance in cSVD patients than established approaches based on whole brain markers. We selected 102 patients (73.7 ± 10.2 years old, 59 males) with MRI-visible SVD lesions and both sMRI and dMRI. Conventional linear models using demographics and established whole brain markers were used as benchmark of predicting individual cognitive scores. Multi-modal metrics of 73 major brain tracts were derived from dMRI and sMRI, and used together with established markers as input of a feed-forward artificial neural network (ANN) to predict individual cognitive scores. A feature selection strategy was implemented to reduce the risk of overfitting. Prediction was performed with leave-one-out cross-validation and evaluated with the R2 of the correlation between measured and predicted cognitive scores. Linear models predicted memory and processing speed with R2 = 0.26 and R2 = 0.38, respectively. With ANN, feature selection resulted in 13 tract-specific metrics and 5 whole brain markers for predicting processing speed, and 28 tract-specific metrics and 4 whole brain markers for predicting memory. Leave-one-out ANN prediction with the selected features achieved R2 = 0.49 and R2 = 0.40 for processing speed and memory, respectively. Our results show proof-of-concept that combining tract-specific multimodal MRI metrics can improve the prediction of cognitive performance in cSVD by leveraging tract-specific multi-modal metrics.

Contrast agent-free state-of-the-art magnetic resonance imaging on cerebral small vessel disease - Part 2: Diffusion tensor imaging and functional magnetic resonance imaging

Cerebral small vessel disease (cSVD) has been widely studied using conventional magnetic resonance imaging (MRI) methods, although the association between MRI findings and clinical features of cSVD is not always concordant. We assessed the additional contribution of contrast agent-free, state-of-the-art MRI techniques, particularly diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI), to understand brain damage and structural and functional connectivity impairment related to cSVD. We performed a review following the PICOS worksheet and Search Strategy, including 152 original papers in English, published from 2000 to 2022. For each MRI method, we extracted information about their contributions regarding the origins, pathology, markers, and clinical outcomes in cSVD. In general, DTI studies have shown that changes in mean, radial, and axial diffusivity measures are related to the presence of cSVD. In addition to the classical deficit in executive functions and processing speed, fMRI studies indicate connectivity dysfunctions in other domains, such as sensorimotor, memory, and attention. Neuroimaging metrics have been correlated with the diagnosis, prognosis, and rehabilitation of patients with cSVD. In short, the application of contrast agent-free, state-of-the-art MRI techniques has provided a complete picture of cSVD markers and tools to explore questions that have not yet been clarified about this clinical condition. Longitudinal studies are desirable to look for causal relationships between image biomarkers and clinical outcomes.

Regional Disruption of White Matter Integrity and Network Connectivity Are Related to Cognition

BACKGROUND

Cognitive impairment is common in the elderly population. Exploring patterns of white matter damage at the microstructural level would give important indications for the underlying mechanisms.

OBJECTIVE

To investigate the spatial patterns of white matter microstructure and structural network alternations in relation to different cognition domainsMethods:Participants from the community-based Shunyi Study were included to investigate the association between white matter measurements and cognition cross-sectionally, via both global and local analysis. Cognitive functions were assessed using digit span, trail making test (TMT)-A/B, Fuld object Memory, and 12-Word Philadelphia Verbal Learning Test (PVLT). White matter measurements including fractional anisotropy (FA), mean diffusivity (MD), and structural network parameters were calculated based on diffusion tensor imaging.

RESULTS

Of the 943 participants included, the mean (SD) age was 55.8 (9.1) years, and the mean (SD) education level was 6.7 (3.2) years. We found the whole set of cognitive measurements was related to diffused white matter microstructural integrity damage and lower global efficiency. Poor executive functions (TMTA/B complete time) were related to lower FA and higher MD predominantly on the anterior white matter skeleton, while verbal memory loss (PVLT test scores) was related to sub-network dysconnectivity in the midline and the right temporal lobe.

CONCLUSION

The anterior brain is dominantly involved in executive dysfunction, while midline and right temporal brain disconnection are more prominent in verbal memory loss. Global and regional disruption of white matter integrity and network connectivity is the anatomical basis of the cognitive impairment in the aging population.

Systematic validation of structural brain networks in cerebral small vessel disease

Cerebral small vessel disease (SVD) is considered a disconnection syndrome, which can be quantified using structural brain network analysis obtained from diffusion MRI. Network analysis is a demanding analysis approach and the added benefit over simpler diffusion MRI analysis is largely unexplored in SVD. In this pre-registered study, we assessed the clinical and technical validity of network analysis in two non-overlapping samples of SVD patients from the RUN DMC study (n = 52 for exploration and longitudinal analysis and n = 105 for validation). We compared two connectome pipelines utilizing single-shell or multi-shell diffusion MRI, while also systematically comparing different node and edge definitions. For clinical validation, we assessed the added benefit of network analysis in explaining processing speed and in detecting short-term disease progression. For technical validation, we determined test-retest repeatability.Our findings in clinical validation show that structural brain networks provide only a small added benefit over simpler global white matter diffusion metrics and do not capture short-term disease progression. Test-retest reliability was excellent for most brain networks. Our findings question the added value of brain network analysis in clinical applications in SVD and highlight the utility of simpler diffusion MRI based markers.

Methodological evaluation of individual cognitive prediction based on the brain white matter structural connectome

An emerging trend is to use regression‐based machine learning approaches to predict cognitive functions at the individual level from neuroimaging data. However, individual prediction models are inherently influenced by the vast options for network construction and model selection in machine learning pipelines. In particular, the brain white matter (WM) structural connectome lacks a systematic evaluation of the effects of different options in the pipeline on predictive performance. Here, we focused on the methodological evaluation of brain structural connectome‐based predictions. For network construction, we considered two parcellation schemes for defining nodes and seven strategies for defining edges. For the regression algorithms, we used eight regression models. Four cognitive domains and brain age were targeted as predictive tasks based on two independent datasets (Beijing Aging Brain Rejuvenation Initiative [BABRI]: 633 healthy older adults; Human Connectome Projects in Aging [HCP‐A]: 560 healthy older adults). Based on the results, the WM structural connectome provided a satisfying predictive ability for individual age and cognitive functions, especially for executive function and attention. Second, different parcellation schemes induce a significant difference in predictive performance. Third, prediction results from different data sets showed that dMRI with distinct acquisition parameters may plausibly result in a preference for proper fiber reconstruction algorithms and different weighting options. Finally, deep learning and Elastic‐Net models are more accurate and robust in connectome‐based predictions. Together, significant effects of different options in WM network construction and regression algorithms on the predictive performances are identified in this study, which may provide important references and guidelines to select suitable options for future studies in this field.

Individual-specific networks for prediction modelling – A scoping review of methods

Background

Recent advances in biotechnology enable the acquisition of high-dimensional data on individuals, posing challenges for prediction models which traditionally use covariates such as clinical patient characteristics. Alternative forms of covariate representations for the features derived from these modern data modalities should be considered that can utilize their intrinsic interconnection. The connectivity information between these features can be represented as an individual-specific network defined by a set of nodes and edges, the strength of which can vary from individual to individual. Global or local graph-theoretical features describing the network may constitute potential prognostic biomarkers instead of or in addition to traditional covariates and may replace the often unsuccessful search for individual biomarkers in a high-dimensional predictor space.

Methods

We conducted a scoping review to identify, collate and critically appraise the state-of-art in the use of individual-specific networks for prediction modelling in medicine and applied health research, published during 2000–2020 in the electronic databases PubMed, Scopus and Embase.

Results

Our scoping review revealed the main application areas namely neurology and pathopsychology, followed by cancer research, cardiology and pathology (N = 148). Network construction was mainly based on Pearson correlation coefficients of repeated measurements, but also alternative approaches (e.g. partial correlation, visibility graphs) were found. For covariates measured only once per individual, network construction was mostly based on quantifying an individual’s contribution to the overall group-level structure. Despite the multitude of identified methodological approaches for individual-specific network inference, the number of studies that were intended to enable the prediction of clinical outcomes for future individuals was quite limited, and most of the models served as proof of concept that network characteristics can in principle be useful for prediction.

Conclusion

The current body of research clearly demonstrates the value of individual-specific network analysis for prediction modelling, but it has not yet been considered as a general tool outside the current areas of application. More methodological research is still needed on well-founded strategies for network inference, especially on adequate network sparsification and outcome-guided graph-theoretical feature extraction and selection, and on how networks can be exploited efficiently for prediction modelling.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01544-6.

Brain White Matter Microstructure as a Risk Factor for Cognitive Decline After Chemotherapy for Breast Cancer

PURPOSE

Cognitive decline is frequently observed after chemotherapy. As chemotherapy is associated with changes in brain white matter microstructure, we investigated whether white matter microstructure before chemotherapy is a risk factor for cognitive decline after chemotherapy.

METHODS

Neuropsychologic tests were administered before and 6 months (n = 49), 2 years (n = 32), and 3 years (n = 32) after chemotherapy in patients with breast cancer receiving anthracycline-based chemotherapy (BC + CT group), at matched intervals to patients with BC who did not receive systemic therapy (BC - CT group: n = 39, 23, and 19, respectively) and to no-cancer controls (NC group: n = 37, 29, and 28, respectively). Using multivariate normative comparison, we evaluated to what extent the cognitive profiles of patients deviated from those of controls. Fractional anisotropy (FA), derived from magnetic resonance diffusion tensor imaging, was used to measure white matter microstructure before treatment. FA was evaluated as a risk factor for cognitive decline, in addition to baseline age, fatigue, cognitive complaints, and premorbid intelligence quotient. We subsequently ran voxel-wise diffusion tensor imaging analyses to investigate white matter microstructure in specific nerve tracts.

RESULTS

Low FA independently predicted cognitive decline early (6 months, P = .013) and late (3 years, P < .001) after chemotherapy. FA did not predict cognitive decline in the BC - CT and NC groups. Voxel-wise analysis indicated involvement of white matter tracts essential for cognitive functioning.

CONCLUSION

Low FA may reflect low white matter reserve. This may be a risk factor for cognitive decline after chemotherapy for BC. If validated in future trials, identification of patients with low white matter reserve could improve patient care, for example, by facilitating targeted, early interventions or even by influencing choices of patients and doctors for receiving chemotherapy.

Cerebral small vessel disease and vascular cognitive impairment: from diagnosis to management

PURPOSE OF REVIEW

We present recent developments in the field of small vessel disease (SVD)-related vascular cognitive impairment, including pathological mechanisms, updated diagnostic criteria, cognitive profile, neuroimaging markers and risk factors. We further address available management and therapeutic strategies.

RECENT FINDINGS

Vascular and neurodegenerative pathologies often co-occur and share similar risk factors. The updated consensus criteria aim to standardize vascular cognitive impairment (VCI) diagnosis, relying strongly on cognitive profile and MRI findings. Aggressive blood pressure control and multidomain lifestyle interventions are associated with decreased risk of cognitive impairment, but disease-modifying treatments are still lacking. Recent research has led to a better understanding of mechanisms leading to SVD-related cognitive decline, such as blood-brain barrier dysfunction, reduced cerebrovascular reactivity and impaired perivascular clearance.

SUMMARY

SVD is the leading cause of VCI and is associated with substantial morbidity. Tackling cardiovascular risk factors is currently the most effective approach to prevent cognitive decline in the elderly. Advanced imaging techniques provide tools for early diagnosis and may play an important role as surrogate markers for cognitive endpoints in clinical trials. Designing and testing disease-modifying interventions for VCI remains a key priority in healthcare.