As a prevalent etiological factor for dementia in the elderly, small vessel disease (SVD) accounts for ~45% of all dementia disorders (Wardlaw et al., 2013a). This disease typically evolves from a stage of no cognitive impairment (NCI), escalates to vascular mild cognitive impairment (VaMCI), and ultimately peaks in subcortical vascular dementia (SVaD). Early diagnosis of VaMCI, or even NCI, carries crucial clinical implications, as cognitive decline tends to be progressive and may become irreversible in the advanced stage of SVaD.

Recognized as key MRI indicators for SVD, white matter hyperintensities (WMHs) have been thoroughly validated (Wardlaw et al., 2013b). Along with adjacent normal-appearing white matter (NAWM), WHMs have demonstrated correlations with cognitive impairment in SVD (Brandhofe et al., 2021). While most SVD research has targeted white matter, emerging studies are exploring alterations in deep gray matter (DGM) regions—namely, basal ganglia and thalamus—which have association with both SVD progression and cognitive impairments (Liu et al., 2015; Bonifazi et al., 2018; Karim et al., 2020).

Chronic global hypoperfusion, a hallmark of SVD, renders DGM regions particularly vulnerable due to their dependence on deep penetrating arteries, leading to progressive neurodegeneration accompanied by robust inflammation (Cao et al., 2021). This inflammation not only accelerates SVD but also triggers iron deposition, amplifying free-radical damage (Andersen et al., 2014; Low et al., 2019). Moreover, it may also disrupt the blood-brain barrier (BBB), causing fluid leakage into perivascular tissues (Kerkhofs et al., 2021). Despite current limitations in pathological research, non-invasive MRI techniques offer a sensitive avenue for in vivo monitoring of these complex changes.

Fractional volume, a metric derived from diffusion tensor based Free-Water (FW) imaging, has been proposed to track changes in extracellular FW, thereby providing insights into underlying pathologies such as neuroinflammation (Pasternak et al., 2012a; Di Biase et al., 2020; Febo et al., 2020). Previous studies demonstrated increased FW values in white matter associated with SVD, suggesting the occurrence of tissue-based neuroinflammation (Duering et al., 2018; Mayer et al., 2022). Intriguingly, increased FW have also been identified as a robust predictor of cognitive function impairment (Gullett et al., 2020). However, few studies have been conducted to investigate FW content specifically within DGM regions in the context of SVD.

The goal of this study was to quantify the alterations in FW values within DGM during early SVD, and to investigate its correlation with cognitive function as a potential biomarker for monitoring SVD progression.

In this longitudinal study, we utilized FW imaging to assess microstructural changes in the DGM of VaMCI, NCI, and HC groups. Three main findings emerged from our analysis. First, cross-sectional comparisons highlighted significant differences in FW values across the three participant groups, following an ascending pattern. Second, the longitudinal assessment revealed a group effect resulted in higher mean FW values within thalamic subregions for the VaMCI group as compared to the NCI group; however, no significant effects were observed for FW values across time or group × time interactions. Third, a significant negative association was observed between the FW value changes in the Pul and the LN of the thalamus, and MoCA score changes in the VaMCI group over 1–2 years. Collectively, these findings indicated the potential of FW as an imaging biomarker during the early stages of SVD.

Compelling evidence indicates that SVD pathology initiates years before clinical SVaD diagnosis, featuring a protracted preclinical or early clinical phase marked by conditions like NCI and VaMCI (Wardlaw et al., 2013a, 2019). In line with previous studies (Low et al., 2019; Evans et al., 2021), our study detected increased FW in DGM of early-stage SVD patients compared to HCs. In the healthy brain, neuronal, vascular, and inflammatory cells collaboratively sustain normal function. However, disrupted interactions among these cells trigger chronic pathological neuroinflammation. Certain risk factors for SVD, such as hypertension and diabetes mellitus, impair cerebral blood flow, damage the BBB, and initiate vascular inflammation. This BBB compromise allows fibrinogen and fluid to leak into the parenchyma, subsequently inducing edema, oxidative stress, and cognitive decline in SVD patients (Evans et al., 2021). Hypertension was identified as the most prevalent risk factor for SVD in our study, which aligns with previous research findings. It potentially plays an important role in the onset/progression of SVD (Meissner, 2016). Diabetes mellitus is also an established risk factor for CSVD (Liu et al., 2018). Diabetes mellitus patients exhibit a significantly greater degree of BBB breakdown compared to control subjects (Janelidze et al., 2017). Elevated levels of pro-inflammatory cytokines, including IL-1β, IL-6, and TNF-α, are often observed in diabetic mellitus patients (Donath and Shoelson, 2011). Therefore, diabetes mellitus probably contributes to the progression from NCI to VaMCI. Interestingly, we further discovered that diabetes mellitus occurred more frequently among VaMCI participants compared to NCI participants who participated in the longitudinal study. This observation might be linked to the higher FW values observed within thalamic subregions in the VaMCI group in the longitudinal assessment.

Conventional in vivo assessment of neuroinflammation remains challenging. Recently, FW imaging, leveraging a bi-tensor diffusion MRI model, offers specific and sensitive quantification of extracellular water (Pasternak et al., 2009; Metzler-Baddeley et al., 2012). This method has demonstrated superior sensitivity over traditional DTI in identifying intra- and inter-group differences (Albi et al., 2017) and has been validated as an accurate marker for neuroinflammation changes (Pasternak et al., 2009, 2012b). In addition, FW correlates with elevated brain interferon-gamma, underscoring its sensitivity to inflammation (Febo et al., 2020). Currently, it is advisable to acquire multi-shell DTI data for FW imaging in order to enhance the accuracy and comprehensiveness of the results (Nemmi et al., 2022). While multi-shell data offers greater precision, its longer acquisition time poses limitations in clinical settings. In our center, most subjects are elderly individuals who cannot endure extended MRI scans. Given the constraints in clinical settings, single-shell DTI is often preferred for its significantly shorter acquisition times, as evidenced in recent large-scale clinical studies (Andica et al., 2019; Zhou et al., 2021; Ray et al., 2023).

In all, our results extend previous findings by suggesting that even in its early stages, SVD features elevated DGM FW values, indicative of underlying neuroinflammation and DGM microstructural pathology. In addition, we found that the VaMCI group had significantly fewer years of education compared with both the NCI and HC groups. The level of education serves as a reliable indicator or proxy for cognitive reserve. Cognitive reserve refers to individual differences in how tasks are performed that may allow some people to be more resilient than others (Stern, 2012). This may be reflected in our results relating to improved attention and executive function in the NCI group longitudinally but stable cognition in the VaMCI group. Our results have taken education into account as control variables. We hope that this can initially reduce the impact of cognitive reserve on the results of our current research. Going forward, we aim to more explicitly integrate measures of cognitive reserve in neuroimaging studies of SVD.

Our longitudinal study reveals that VaMCI patients exhibit high mean FW values in the LN, Pul, and IML regions of the thalamus compared to NCI patients. Multimodal neuroimaging has already pinpointed early-stage thalamic microstructural changes in SVD, including reduced cerebral blood flow (Kato et al., 2008), axonal degeneration (Cavallari et al., 2014), and other markers such as decreased volume (Wang et al., 2020), increased diffusion (Öztoprak et al., 2015) and iron deposition (Sun et al., 2020). The thalamus, with its cortical, subcortical, and cerebellar connections, is a critical node in brain networks supporting cognitive functions known to decline in SVD, including component processes of executive functions of attention, information processing and memory (Fama and Sullivan, 2015). As such, damage to the thalamic nuclei potentially impairs a wide range of neurologic functions that may clinically translate into significant cognitive disability, which is primarily affected in the early stages of SVD (Duering et al., 2011; Bonifazi et al., 2018). However, whole-thalamic analyses fall short in capturing subregional characteristics, often because they aggregate data, diluting subtle variance at the subregional level. Thus, our study considered the structural complexity of the thalamus itself and the functional subspecialization of its nuclei. Our results indicate that elevated FW values in the LN, Pul and IML of the thalamus likely play key roles in SVD-related cognitive decline. Furthermore, FW alterations remained stable in both groups during the follow-up time. The longitudinal dimension of the current study adds a contribution to the literature, by showing that higher FW remains unchanged during the 1–2 years of VaMCI and NCI. We speculate that there could be no evidence of significant progression or remission in neuroinflammation in the early stage of SVD. Our results are consistent with previous studies showing that the inflammatory response is sustained in the long term and longitudinally associated with SVD progression (Low et al., 2019). Interestingly, we still found a trend toward increased FW in the NCI group over 1–2 years, and the NCI group showed a faster rate of increase than the VaMCI group, explaining the lack of significant FW differences between the VaMCI and NCI groups at follow-up. However, given the small sample size at follow up mandates cautious interpretation of these results.

In addition, our results also demonstrated an association between increasing FW in the Pul and LN of the thalamus and decreasing cognitive function over 1–2 years in the VaMCI group. This aligns with longitudinal study showing elevated inflammatory markers could predict SVD severity and progression (Low et al., 2019). In the realm of cognition, the Pul modulates attentional synchrony between cortical areas (Saalmann et al., 2012), while the LN act as a tunable filter serving as a cognitive control node (Halassa and Kastner, 2017). Previous studies substantiate FW as a strong MRI marker for cognitive deceleration (Berger et al., 2022) and as a disease-progression biomarker in Parkinson's disease (Ofori et al., 2015; Zhou et al., 2021). Our findings thus suggest that increased FW in the thalamus may represent a potential marker for disease course. Despite the increasing global burden of SVD and its role in dementia, established treatments remain elusive (Smith and Markus, 2020). Current interventions focus on risk management, offering limited efficacy. Given this backdrop, our study amplifies the relevance of FW values as potential neuroinflammatory marker in DGM at the preclinical and early clinical stage of SVD, offering a pathway toward targeted therapeutic interventions that may directly affect early-stage of SVD cognitive outcomes.

This study has several limitations. First, the sample size in the follow-up study was relatively small. Only 51 SVD patients with complete MRI scans and neuropsychological assessments both at baseline and at follow-up were included. The absence of healthy controls in the follow-up study further compromises our analysis. Second, our methodology describes the change in DGM over time using FW imaging but limits its scope to just two-time points. Third, the diffusion imaging data, limited to 20 directions, falls short of cutting-edge standards. Recently, the acquisition of multi-shell DTI data is recommended in FW imaging for enhanced robustness and precision. Moreover, the interpretation of single-shell DTI-FW in GM is inherently more complex and that further pathological validation is necessary to enhance our understanding of these measurements. Fourth, the impact of subtle head movements caused by cognitive dysfunction on the MRI images remains a significant concern. Fifth, FDR correction was not applied in the longitudinal part of our study as this was intended to be an exploratory analysis. Thus, future studies should aim to expand the cohort size, extend the study duration, and employ state-of-art imaging techniques to provide a more comprehensive understanding of longitudinal FW changes.

In summary, the present study showed that increased FW level in DGM emerge at the preclinical and early clinical stages of SVD and longitudinally remain persistent for 1–2 years before the onset of clinical deterioration. Through FW imaging, we offer an indirect measurement of neuroinflammation, leading credence to the hypothesis that diffuse neuroinflammation underpins SVD activity. Furthermore, the FW value changes within thalamic subregions could serve as an imaging biomarker for the mechanism of cognitive decline in SVD progression. Our findings may have potential implications for therapeutic interventions for SVD.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

The studies involving humans were approved by the Research Ethics Committee of the Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

YS: Conceptualization, Formal analysis, Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing. XH: Formal analysis, Investigation, Methodology, Writing – review & editing. ZL: Data curation, Formal analysis, Methodology, Writing – review & editing. YQ: Formal analysis, Investigation, Methodology, Project administration, Validation, Writing – review & editing. YH: Formal analysis, Investigation, Methodology, Project administration, Visualization, Writing – review & editing. YZha: Methodology, Validation, Writing – review & editing. YD: Writing – review & editing, Writing – original draft. HW: Conceptualization, Formal analysis, Methodology, Writing – review & editing. QX: Conceptualization, Funding acquisition, Writing – review & editing. YZho: Conceptualization, Funding acquisition, Supervision, Writing – review & editing.