Early-Stage White Matter Lesions Detected by Multispectral MRI Segmentation Predict Progressive Cognitive Decline

White matter lesions (WML) are the main brain imaging surrogate of cerebral small-vessel disease. A new MRI tissue segmentation method, based on a discriminative clustering approach without explicit model-based added prior, detects partial WML volumes, likely representing very early-stage changes in normal-appearing brain tissue. This study investigated how the different stages of WML, from a “pre-visible” stage to fully developed lesions, predict future cognitive decline. MRI scans of 78 subjects, aged 65–84 years, from the Leukoaraiosis and Disability (LADIS) study were analyzed using a self-supervised multispectral segmentation algorithm to identify tissue types and partial WML volumes. Each lesion voxel was classified as having a small (33%), intermediate (66%), or high (100%) proportion of lesion tissue. The subjects were evaluated with detailed clinical and neuropsychological assessments at baseline and at three annual follow-up visits. We found that voxels with small partial WML predicted lower executive function compound scores at baseline, and steeper decline of executive scores in follow-up, independently of the demographics and the conventionally estimated hyperintensity volume on fluid-attenuated inversion recovery images. The intermediate and fully developed lesions were related to impairments in multiple cognitive domains including executive functions, processing speed, memory, and global cognitive function. In conclusion, early-stage partial WML, still too faint to be clearly detectable on conventional MRI, already predict executive dysfunction and progressive cognitive decline regardless of the conventionally evaluated WML load. These findings advance early recognition of small vessel disease and incipient vascular cognitive impairment.

[1]  Colin M. Macleod Half a century of research on the Stroop effect: an integrative review. , 1991, Psychological bulletin.

[2]  Koenraad Van Leemput,et al.  Automated segmentation of multiple sclerosis lesions by model outlier detection , 2001, IEEE Transactions on Medical Imaging.

[3]  Anke Meyer-Bäse,et al.  Fully automated biomedical image segmentation by self-organized model adaptation , 2004, Neural Networks.

[4]  Koen L. Vincken,et al.  Brain atrophy and cognition: Interaction with cerebrovascular pathology? , 2011, Neurobiology of Aging.

[5]  José V. Manjón,et al.  Improved estimates of partial volume coefficients from noisy brain MRI using spatial context , 2010, NeuroImage.

[6]  M. Lawton,et al.  Assessment of Older People: Self-Maintaining and Instrumental Activities of Daily Living , 1969 .

[7]  Ricardo Vigário,et al.  Self-Supervised MRI Tissue Segmentation by Discriminative Clustering , 2014, Int. J. Neural Syst..

[8]  B. Ginneken,et al.  3D Segmentation in the Clinic: A Grand Challenge , 2007 .

[9]  P. Scheltens,et al.  Confirmatory factor analysis of the Neuropsychological Assessment Battery of the LADIS study: A longitudinal analysis , 2013, Journal of clinical and experimental neuropsychology.

[10]  S. Ferris General Measures of Cognition , 2003, International Psychogeriatrics.

[11]  Frederik Barkhof,et al.  Progression of White Matter Hyperintensities and Incidence of New Lacunes Over a 3-Year Period: The Leukoaraiosis and Disability Study , 2008, Stroke.

[12]  Jerry L. Prince,et al.  Partial volume estimation and the fuzzy C-means algorithm [brain MRI application] , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[13]  H E GRIFFITHS,et al.  Analysis of Function , 1947, Occupational therapy and rehabilitation.

[14]  R. Reitan Validity of the Trail Making Test as an Indicator of Organic Brain Damage , 1958 .

[15]  Alan C. Evans,et al.  Automatic "pipeline" analysis of 3-D MRI data for clinical trials: application to multiple sclerosis , 2002, IEEE Transactions on Medical Imaging.

[16]  Owen Carmichael,et al.  White Matter Hyperintensity Penumbra , 2011, Stroke.

[17]  M. O’Sullivan,et al.  Activate your online subscription , 2001, Neurology.

[18]  M. Lawton,et al.  Assessment of older people: self-maintaining and instrumental activities of daily living. , 1969, The Gerontologist.

[19]  Stephen M. Smith,et al.  Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.

[20]  P. Scheltens,et al.  Impact of Age-Related Cerebral White Matter Changes on the Transition to Disability – The LADIS Study: Rationale, Design and Methodology , 2004, Neuroepidemiology.

[21]  P. Scheltens,et al.  2001–2011: A Decade of the LADIS (Leukoaraiosis And DISability) Study: What Have We Learned about White Matter Changes and Small-Vessel Disease? , 2011, Cerebrovascular Diseases.

[22]  Alfredo Vellido,et al.  Semi-Supervised Analysis of Human Brain Tumours from Partially Labeled MRS Information, Using Manifold Learning Models , 2011, Int. J. Neural Syst..

[23]  John A. D. Aston,et al.  MR Image Segmentation Using a Power Transformation Approach , 2009, IEEE Transactions on Medical Imaging.

[24]  Owen Carmichael,et al.  FLAIR and Diffusion MRI Signals Are Independent Predictors of White Matter Hyperintensities , 2013, American Journal of Neuroradiology.

[25]  Koenraad Van Leemput,et al.  A unifying framework for partial volume segmentation of brain MR images , 2003, IEEE Transactions on Medical Imaging.

[26]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[27]  Koenraad Van Leemput,et al.  Automated model-based tissue classification of MR images of the brain , 1999, IEEE Transactions on Medical Imaging.

[28]  Rainer Goebel,et al.  Analysis of functional image analysis contest (FIAC) data with brainvoyager QX: From single‐subject to cortically aligned group general linear model analysis and self‐organizing group independent component analysis , 2006, Human brain mapping.

[29]  J. Ferro,et al.  Brain atrophy accelerates cognitive decline in cerebral small vessel disease , 2012, Neurology.

[30]  A. Hofman,et al.  Cerebral microbleeds are associated with worse cognitive function , 2012, Neurology.

[31]  Karl J. Friston,et al.  Unified segmentation , 2005, NeuroImage.

[32]  W. M. van der Flier,et al.  Incident lacunes influence cognitive decline , 2011, Neurology.

[33]  P. Scheltens,et al.  Diffusion changes predict cognitive and functional outcome: The LADIS study , 2013, Annals of neurology.

[34]  S. Folstein,et al.  "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. , 1975, Journal of psychiatric research.

[35]  W. Mali,et al.  Vascular brain lesions, brain atrophy, and cognitive decline. The Second Manifestations of ARTerial disease—Magnetic Resonance (SMART-MR) study , 2014, Neurobiology of Aging.

[36]  P. Scheltens,et al.  Impact of White Matter Hyperintensities Scoring Method on Correlations With Clinical Data: The LADIS Study , 2006, Stroke.

[37]  W. M. van der Flier,et al.  Diffusion-Weighted Imaging and Cognition in the Leukoariosis and Disability in the Elderly Study , 2010, Stroke.

[38]  D. Harvey,et al.  Longitudinal Changes in Memory and Executive Functioning are Associated with longitudinal change in instrumental activities of daily living in older Adults , 2009, The Clinical neuropsychologist.

[39]  K. Jellinger,et al.  Heterogeneity in age-related white matter changes , 2011, Acta Neuropathologica.