On the stability of canonical correlation analysis and partial least squares with application to brain-behavior associations

Associations between datasets can be discovered through multivariate methods like Canonical Correlation Analysis (CCA) or Partial Least Squares (PLS). A requisite property for interpretability and generalizability of CCA/PLS associations is stability of their feature patterns. However, stability of CCA/PLS in high-dimensional datasets is questionable, as found in empirical characterizations. To study these issues systematically, we developed a generative modeling framework to simulate synthetic datasets. We found that when sample size is relatively small, but comparable to typical studies, CCA/PLS associations are highly unstable and inaccurate; both in their magnitude and importantly in the feature pattern underlying the association. We confirmed these trends across two neuroimaging modalities and in independent datasets with n ≈ 1000 and n = 20,000, and found that only the latter comprised sufficient observations for stable mappings between imaging-derived and behavioral features. We further developed a power calculator to provide sample sizes required for stability and reliability of multivariate analyses. Collectively, we characterize how to limit detrimental effects of overfitting on CCA/PLS stability, and provide recommendations for future studies.

Medio-dorsal thalamic dysconnectivity in chronic knee pain: A possible mechanism for negative affect and pain comorbidity

The reciprocal interaction between pain and negative affect is acknowledged but pain-related alterations in brain circuits involved in this interaction, such as the mediodorsal thalamus (MDThal), still require a better understanding. We sought to investigate the relationship between MDThal circuitry, negative affect and pain severity in chronic musculoskeletal pain. For these analyses, participants with chronic knee pain (CKP, n = 74) and without (n = 36) completed magnetic resonance imaging scans and questionnaires. Seed-based MDThal functional connectivity (FC) was compared between groups. Within CKP group, we assessed the interdependence of MDThal FC with negative affect. Finally, post hoc moderation analysis explored whether burden of pain influences affect-related MDThal FC. The CKP group showed altered MDThal FC to hippocampus, ventromedial prefrontal cortex and subgenual anterior cingulate. Furthermore, in CKP group, MDThal connectivity correlated significantly with negative affect in several brain regions, most notably the medial prefrontal cortex, and this association was stronger with increasing pain burden and absent in pain-free controls. In conclusion, we demonstrate mediodorsal thalamo-cortical dysconnectivity in chronic pain with areas linked to mood disorders and associations of MDThal FC with negative affect. Moreover, burden of pain seems to enhance affect sensitivity of MDThal FC. These findings suggest mediodorsal thalamic network changes as possible drivers of the detrimental interplay between chronic pain and negative affect.

Mapping brain endophenotypes associated with idiopathic pulmonary fibrosis genetic risk

BACKGROUND

Idiopathic pulmonary fibrosis (IPF) is a serious disease of the lung parenchyma. It has a known polygenetic risk, with at least seventeen regions of the genome implicated to date. Growing evidence suggests linked multimorbidity of IPF with neurodegenerative or affective disorders. However, no study so far has explicitly explored links between IPF, associated genetic risk profiles, and specific brain features.

METHODS

We exploited imaging and genetic data from more than 32,000 participants available through the UK Biobank population-level resource to explore links between IPF genetic risk and imaging-derived brain endophenotypes. We performed a brain-wide imaging-genetics association study between the presence of 17 known IPF risk variants and 1248 multi-modal imaging-derived features, which characterise brain structure and function.

FINDINGS

We identified strong associations between cortical morphological features, white matter microstructure and IPF risk loci in chromosomes 17 (17q21.31) and 8 (DEPTOR). Through co-localisation analysis, we confirmed that cortical thickness in the anterior cingulate and more widespread white matter microstructure changes share a single causal variant with IPF at the chromosome 8 locus. Post-hoc preliminary analysis suggested that forced vital capacity may partially mediate the association between the DEPTOR variant and white matter microstructure, but not between the DEPTOR risk variant and cortical thickness.

INTERPRETATION

Our results reveal the associations between IPF genetic risk and differences in brain structure, for both cortex and white matter. Differences in tissue-specific imaging signatures suggest distinct underlying mechanisms with focal cortical thinning in regions with known high DEPTOR expression, unrelated to lung function, and more widespread microstructural white matter changes consistent with hypoxia or neuroinflammation with potential mediation by lung function.

FUNDING

This study was supported by the NIHR Nottingham Biomedical Research Centre and the UK Medical Research Council.

Anticholinergic drugs and forebrain magnetic resonance imaging changes in cognitively normal people and those with mild cognitive impairment

Background and purpose

Anticholinergic (AC) medication use is associated with cognitive decline and dementia, which may be related to an AC‐induced central hypocholinergic state, but the exact mechanisms remain to be understood. We aimed to further elucidate the putative link between AC drug prescription, cognition, and structural and functional impairment of the forebrain cholinergic nucleus basalis of Meynert (NBM).

Methods

Cognitively normal (CN; n = 344) and mildly cognitively impaired (MCI; n = 224) Alzheimer’s Disease Neuroimaging Initiative Phase 3 participants with good quality 3‐T magnetic resonance imaging were included. Structural (regional gray matter [GM] density) and functional NBM integrity (functional connectivity [FC]) were compared between those on AC medication for > 1 year (AC⁺) and those without (AC⁻) in each condition. AC burden was classed as mild, moderate, or severe.

Results

MCI AC⁺ participants (0.55 ± 0.03) showed lower NBM GM density compared to MCI AC⁻ participants (0.56 ± 0.03, p = 0.002), but there was no structural AC effect in CN. NBM FC was lower in CN AC⁺ versus CN AC⁻ (3.6 ± 0.5 vs. 3.9 ± 0.6, p = 0.001), and in MCI AC⁺ versus MCI AC⁻ (3.3 ± 0.2 vs. 3.7 ± 0.5, p < 0.001), with larger effect size in MCI. NBM FC partially mediated the association between AC medication burden and cognition.

Conclusions

Our findings provide novel support for a detrimental effect of mild AC medication on the forebrain cholinergic system characterized as functional central hypocholinergic that partially mediated AC‐related cognitive impairment. Moreover, structural tissue damage suggests neurodegeneration, and larger effect sizes in MCI point to enhanced susceptibility for AC medication in those at risk of dementia.

Neonatal brain resting-state functional connectivity imaging modalities

Infancy is the most critical period in human brain development. Studies demonstrate that subtle brain abnormalities during this state of life may greatly affect the developmental processes of the newborn infants. One of the rapidly developing methods for early characterization of abnormal brain development is functional connectivity of the brain at rest. While the majority of resting-state studies have been conducted using magnetic resonance imaging (MRI), there is clear evidence that resting-state functional connectivity (rs-FC) can also be evaluated using other imaging modalities. The aim of this review is to compare the advantages and limitations of different modalities used for the mapping of infants' brain functional connectivity at rest. In addition, we introduce photoacoustic tomography, a novel functional neuroimaging modality, as a complementary modality for functional mapping of infants' brain.

Structured and Sparse Canonical Correlation Analysis as a Brain-Wide Multi-Modal Data Fusion Approach

Multi-modal data fusion has recently emerged as a comprehensive neuroimaging analysis approach, which usually uses canonical correlation analysis (CCA). However, the current CCA-based fusion approaches face problems like high-dimensionality, multi-collinearity, unimodal feature selection, asymmetry, and loss of spatial information in reshaping the imaging data into vectors. This paper proposes a structured and sparse CCA (ssCCA) technique as a novel CCA method to overcome the above problems. To investigate the performance of the proposed algorithm, we have compared three data fusion techniques: standard CCA, regularized CCA, and ssCCA, and evaluated their ability to detect multi-modal data associations. We have used simulations to compare the performance of these approaches and probe the effects of non-negativity constraint, the dimensionality of features, sample size, and noise power. The results demonstrate that ssCCA outperforms the existing standard and regularized CCA-based fusion approaches. We have also applied the methods to real functional magnetic resonance imaging (fMRI) and structural MRI data of Alzheimer's disease (AD) patients (n = 34) and healthy control (HC) subjects (n = 42) from the ADNI database. The results illustrate that the proposed unsupervised technique differentiates the transition pattern between the subject-course of AD patients and HC subjects with a p-value of less than 1×10^-6 . Furthermore, we have depicted the brain mapping of functional areas that are most correlated with the anatomical changes in AD patients relative to HC subjects.