Table 2 Summary of included articles analysing resting-state functional connectivity in healthy participants or patients without vascular cognitive impairment. We report imaging and clinical characteristics of patients included in each study, key steps in the acquisition and pre-processing of BOLD data, analysis of functional connectivity, and FC patterns found to be associated with CSVD. Descriptive statistics as extracted from articles are reported as range (min–max) and/or mean ± standard deviation. Missing information is indicted by empty brackets ([]). Reported are clinical characteristics of patients included in each study, details about the quantification of white matter hyperintensities, key steps in the analysis of functional connectivity, and FC patterns found to be associated with CSVD. Arrows indicate increased (↑) or reduced (↓) values, as well as positive (↗) and negative (↘) associations

[69]

12 depression

12 HC

Fuzzy connected algorithm [70]

GE Signa, 1.5 T

TR 2000 ms, TE 35 ms

64 × 64 × 26, 3.75 × 3.75 × 3.8 mm³

[volumes], eyes open

AFNI

[Confound regression]

[Motion scrubbing]

PCC from ext. template

SCA to define DMN

Pearson correlation

FC in DMN ↘ WMH in medial PFC

[71]

47 depression

46 HC

Not reported

Siemens Trio, 3 T

TR 2000 ms, TE 32 ms

128 × 128 × 28, 2 × 2 × 2 mm³

150 volumes, eyes open

SPM 5

[Confound regression]

[Motion scrubbing]

PCC from ext. template

SCA to define DMN

Pearson correlation

↓ Association between DMN-FC and treatment response after controlling for WMH load

[62]

13 early AD

17 MCI

14 HC

Semi-automatic using FireVoxel [72]

[Scanner]

TR 3000 ms, TE 30 ms

[matrix], 3.3 × 3.3 × 3.3 mm³

140 volumes, [eyes]

[Confound regression]

[Motion scrubbing]

Medial PFC and PCC from ext. atlas

SCA to define DMN

fALFF

Both increased WMH load and reduced DMN-FC in AD and MCI compared to HC

[73]

100 MCI

In-house automatic pipeline [74]

Philips Achieva, 3 T

TR 2000 ms, TE 30 ms

[matrix], [resolution]

200 volumes

eyes closed

DPARSF

Confound regression

- 6 motion parameters

- GSR: CSF, WM, global

[Motion scrubbing]

SCA from the hippocampus and PCC

No association between WMH load and FC

[63]

43 MCI

24 HC

Histogram segmentation [75]

Training

Philips, 3 T, 8-channel head coil

TR 3000 ms, [TE]

[matrix], 3.3 × 3.3 × 3.3 mm³

140 volumes

eyes open

Testing

Siemens Verio, 3 T, 32-channel head coil

TR 2580 ms, [TE]

[matrix], 3.5 × 3.5 × 3.5 mm³

180 volumes

eyes closed

DARTEL

Confound regression

- 6 motion parameters

- GSR: CSF, WM

[Motion scrubbing]

Whole-brain SCA

Pearson correlation

No association between WMH and FC

[65]

90 MCI

140 HC

SPM Lesion Segmentation Tool [76]

Siemens Trio, 3 T

TR 2300 ms, TE 30 ms

[] × [] × 34, 3 × 3 × 4 mm³

[volumes]

eyes closed

CONN, SPM 12

Confound regression

- compCor

- GSR: CSF, WM

Motion scrubbing

- Artefact Detection Tools

- Spike regression (FD > 0.5 mm)

Preselected cognitive control networks (FPCN, SN) + DMN

Pearson correlation

Structural equation modelling

Weaker negative association between executive function/memory and WMH load in patients with high global FC

[77]

18 svMCI + depression

17 svMCI − depression

23 HC

Fazekas scale

GE MR750, 3 T

TR 2000 ms, TE 35 ms

64 × 64 × 26, 4 × 4 × 4 mm³

240 volumes

[eyes]

DPABI

Confound regression

- Linear and quadratic trends, 24p

- GSR: CSF, WM, global

Motion scrubbing

- Head motion > 3 mm/3°

- Volume censoring (FD > 0.5 mm)

VBM

SCA from altered regions

Pearson correlation

↑ FC between right middle cingulate cortex and right parahippocampal gyrus

[78]

38 P w Sjogren syndrome

40 HC

Wahlund score

Siemens Trio, 3 T

TR 2500 ms, TE 30 ms

96 × 96 × 40, 2.3 × 2.3 × 3 mm³

204 volumes

eyes closed

Matlab, DPABI

Confound regression

- Linear trend

- GSR: CSF, WM, global

Motion scrubbing

- Mean FD > 0.2 mm

SCA from hippocampi

Pearson correlation

FC ↗ WMH left hippocampus and right inf. orbital and inf. temporal gyrus

 [79]

76 healthy participants

Mixture model [80]

GE Signa, 1.5 T

TR 2000 ms, TE 40 ms

[] × [] × 24, [] × [] × 5 mm³

240 volumes

[eyes]

REST

Confound regression

- Head motion parameters

- GSR: CSF,WM, global

Motion scrubbing

- > 58 outlier volumes (> 1.5 mm/1.5°)

PCC from ext. template

SCA to define DMN

Pearson correlation

No association between WMH load and FC

Episodic memory ↗ medial PFC–left inferior parietal cortex FC in patients with low grey matter volume

 [81]

127 healthy (Harvard Ageing Brain Study)

Fazekas

0–1 vs. 2–3

Siemens Trio, 3 T, 12-channel head coil

TR 3000 ms, TE 30 ms

72 × 72 × [], 3 × 3 × 3 mm³

124 volumes

eyes open

SPM 8

Confound regression

- Realignment params + derivatives

- GSR: WM, CSF, global

Motion scrubbing

- Mean FD > 0.15 mm

- ≥ 20 outlier volumes (> 0.75 mm/1.5°)

PCC and medial PFC from external DMN template

Partial Pearson correlation

Probabilistic tractography

↓ Association between PCC-medial PFC FC and mean diffusivity in cingulum bundle

 [82]

186 clinically healthy (Harvard Ageing Brain Study)

Automated fuzzy-connected algorithm [70]

Simens Trio, 3 T, 12-channel head coil

TR 3000 ms, TE 30 ms

72 × 72 × 47, 3 × 3 × 3 mm³

2 × 124 volumes

eyes open

SPM 8

Confound regression

- 12 motion parameters

Motion scrubbing

- ‘mean movement’ > 0.2 mm

- > 20 outlier volumes (> 0.75 mm/1.5°)

Template-based rotation to define DMN and FPCN

Pearson correlation

No association between WMH load and FC

 [83]

51 healthy participants

SPM Lesion Segmentation Tool [76]

Phillips Ingenia, 3 T

TR 2600 ms, TE 35 ms

128 × 128 × 35, 1.8 × 1.8 × 4 mm³

125 volumes, [eyes]

REST, GIFT

[Confound regression]

[Motion scrubbing]

ICA to define DMN, SN, FPN, VN

FC in DMN ↗ WMH in the mediotemporal complex

FC in SN ↗ WMH in the right S1 and sup./inf. parietal cortex

 [84]

1584 healthy participants

(Rotterdam Study)

Tract-specific WMH load [85]

GE Signa, 1.5 T

TR 2900 ms, TE 60 ms

64 × 64 × 31, 3.3 × 3.3 × 3.3 mm³

160 volumes, eyes open

FSL

Confound regression

- Low-frequency drifts

- Motion components

- ICA

Motion scrubbing

- Max FD > 0.5 mm, abs. motion > 3 mm

Desikan–Killiany parcellation

Pearson correlation

Probabilistic tractography

FC ↘ WMH both tract-specific and global

 [86]

145 healthy participants

SPM Lesion Segmentation Tool [76]

GE MR750, 3 T

TR 1500 ms, TE 27 ms

64 × 64 × 29, 3.75 × 3.75 × 4 mm³

162 volumes

eyes open

FSL

Confound regression

- GSR: CSF, WM

Motion scrubbing

- FD > 0.5 mm

ICA to define DMN, SMN, FPCN

Pearson correlation

No association between WMH load and FC

 [87]

69 healthy participants

Coarse-to-fine in-house developed mathematical morphology method [88]

Phillips Achieva, 3 T

TR 2050 ms, TE 25 ms

64 × 64 × 47, 3.2 × 3.2 × 3.2 mm³

210 volumes

eyes open

CONN

Confound regression

- 6 motion parameters

- GSR: WM, CSF

[Motion scrubbing]

AAL atlas, DTI atlas

Whole-brain SCA

Intrinsic connectivity contrast

FC in the left cuneus and right sup. occipital cortex ↗ WMH in the right ant. corona radiata

FC in the left superior occipital cortex ↗ WMH in the right superior corona radiata

 [67]

400 healthy participants

(Baltimore Longitudinal Study of Aging)

Multimodal supervised classification algorithm [89]

Philips Achieva, 3 T

TR 2000 ms, TE 30 ms

[matrix], 3 × 3 × 3 mm³

180 volumes

[eyes]

Confound regression

- 24 motion parameters

- GSR: global, WM, CSF

Motion scrubbing

- ‘summary motion value’ > 0.2 mm

- Volume censoring (FD > 0.5 mm, <  5 min)

Geodesic graph-based segmentation

Regional homogeneity

Sparse connectivity patterns

Pattern of advanced brain ageing characterised by both increased WMH burden and reduced FC compared to resilient agers

 [66]

11 healthy participants

Automated regression algorithm [90] using a Hidden Markov Random Field with Expectation Maximization [91]

Siemens Trio, 3 T

TR 2000 ms, TE 27 ms

92 × 92 × 43, 2.5 × 2.5 × 3 mm³

240 volumes

eyes closed

SPM12

Confound regression

- Linear/quadratic, 18 motion parameters

- GSR: CSF, WM

Motion scrubbing

-> 3 mm max, >  3° max

-> 24 spikes (FD > 1 mm)

Brainnetome atlas (228)

Graph theory to define DMN

Pearson correlation

No association between WMH load and DMN FC trajectories

 [92]

562 healthy participants

SPM Lesion Segmentation Tool [76]

Phillips Achieva, 3 T

TR 2000 ms, TE 20 ms

112 × 112 × 37, 2 × 2 × 3 mm³

[volumes], [eyes]

[Confound regression]

[Motion scrubbing]

Mean FD as covariate in analysis

Desikan–Killiany parcellation

FC measure not specified

No association between WMH load and FC

 [93]

182 participants

(UK Biobank)

BIANCA with manual correction [94]

Siemens Skyra, 3 T

TR 735 ms, TE 39 ms

88 × 88 × 64, 2.4 × 2.4 × 2.4 mm³

490 volumes, [eyes]

FMRIB (FSL), ICA-FIX

Confound regression

- ICA

[Motion scrubbing]

ICA, AAL atlas

Pearson correlation

Degree centrality

FC ↗ WMH in right orbitofrontal cortex

 [95]

250 healthy

(Harvard Aging Brain Study)

Automated fuzzy-connected algorithm [70]

Siemens Trio, 3 T

TR 3000 ms, TE 30 ms

[matrix], 3 × 3 × 3 mm³

2 × 124 volumes, eyes open

SPM 8

Template-based rotation method

[Confound regression]

[Motion scrubbing]

Template-based rotation to define DMN, SMN, DMN, and FPCN

Pearson correlation

Association between WMH load and FC not investigated

FC in DMN ↘ risk of progression to MCI