Clinical neuroscience research最新文献

Functional neuroimaging, such as positron emission tomography (PET) and single photon emission computed tomography (SPECT), provides a valuable technique for detecting regional changes in brain metabolic activity and blood flow associated with mild cognitive impairment (MCI) and dementia. Multivariate analysis techniques have recently received increasing attention. The results of multivariate analysis can be more easily interpreted as a signature of neuronal networks, which lend themselves to prospective application of results from the analysis of one dataset to entirely new datasets. They are well placed to provide information about mean differences and correlations with behavior with potentially greater statistical power and better reproducibility. This article will focus on investigating the baseline and progression of MCI using functional brain imaging techniques and multivariate analysis in order to understand the genesis and natural history of cognitive impairment in Alzheimer’s disease (AD) and Parkinson’s disease (PD), respectively.

As clinical and cognitive neurosciences mature, the need for sophisticated neuroimaging analysis becomes more apparent. Multivariate analysis techniques have recently received increasing attention because they have attractive features that cannot be easily realized by the more commonly used univariate, voxel-wise, techniques. Multivariate approaches evaluate correlation/covariance of activation across brain regions, rather than proceeding on a voxel-by-voxel basis. Thus, their results can be more easily interpreted as a signature of neural networks. Univariate approaches, in contrast, cannot directly address functional connectivity in the brain. Apart from this conceptual difference, the covariance approach can also result in greater statistical power when compared with univariate techniques, which are forced to employ very stringent, and often overly conservative, corrections for voxel-wise multiple comparisons. Multivariate techniques also lend themselves much better to prospective application of results from the analysis of one dataset to entirely new datasets. We provide two examples that illustrate different uses of multivariate techniques in cognitive and clinical neuroscience. We hope this contribution helps facilitate wider dissemination of these techniques in the research community.

Although approximately 1–3% of the population over age 65 have Parkinson’s disease (PD), only about 75% of the patients diagnosed with parkinsonism have PD. The differential diagnosis of parkinsonian disorders based on clinical symptoms alone is particularly difficult during the early stages of the disease. A number of imaging strategies have been developed to differentiate between these clinically similar conditions. The assessment of abnormal patterns of brain metabolism, either by visual inspection or using computer-assisted algorithms, can be used to discriminate between classical PD and atypical variant conditions such as multiple system atrophy (MSA), progressive supranuclear palsy (PSP), or corticobasal ganglionic degeneration (CBGD).

Recent advances in network quantification routines have created the basis for fully automated differential diagnosis. Using PET, investigators have identified specific disease-related spatial covariance patterns that are characteristic of PD and its variants. By computing pattern expression in individual patient scans, it has become possible to determine the likelihood of a specific diagnosis. In this review, we describe the various imaging techniques that have been used to diagnose PD with emphasis on the application of network tools. Analogous methods may have value in the assessment of other neurodegenerative and neuropsychiatric conditions.

Idiopathic torsion dystonia and Huntington’s disease represent autosomal dominant inherited hyperkinetic disorders that vary considerably with regard to pathologic mechanisms and clinical penetrance. To study these mechanisms, non-manifesting DYT1 mutation carriers (nmDYT1) and presymptomatic HD gene carriers (p-HD), as well as age-matched controls were scanned with (i) FDG PET in the resting state, and (ii) ¹⁵O-labeled water PET while performing a motor sequence learning task and motor execution task. We used voxel-based principal components analysis (PCA) to isolate the effects of genotype on regional metabolism in the resting state and on patterns of activation during motor performance.

We detected two specific genotype-related metabolic patterns in the resting state. The torsion dystonia-related pattern was characterized by hypermetabolism of the basal ganglia, cerebellum and the supplementary motor area (SMA). By contrast, the voxel-based HD-related pattern displayed reductions in caudate and putamen metabolic activity associated with relative increases in the thalamus, the calcarine cortex, and primary motor cortex.

Both mutations were associated with sequence learning deficits compared to controls. Carriers of each genotype activated different networks while performing the sequence learning task. In nmDYT1, learning was associated with activation of a specific network involving the cerebellar hemispheres, the left pre-SMA and anterior cingulate region, as well as inferior prefrontal and occipital association cortices. In p-HD, the learning network involved activation of the orbitofrontal and occipital association regions bilaterally, the left mediodorsal thalamus, and the right cerebellar hemisphere.

These topographies illustrate the utility of PCA in identifying functional abnormalities in fronto-striatal loops and related pathways.

Network approaches to analysis of functional neuroimaging data provide a powerful means with which to understand the complex functioning of the brain in health and disease. To illustrate how such approaches can be used to investigate sex differences in neurocognition, we applied the multivariate technique of Principal Components Analysis (PCA) to an fMRI dataset obtained during performance of mental rotation – a classic visuospatial task known to give rise to sex differences in performance. In agreement with prior results obtained using univariate methods, PCA identified a core mental rotation network (principal component [PC]1, accounting for 53.1% of total variance) that included activation of bilateral frontal, parietal, occipital and occipitotemporal regions. Expression of PC1 was similar in men and women, and was positively correlated with level of education. PC2, which accounted for 5.7% of total variance, was differentially expressed by men and women, and indicated greater mental rotation-associated neural activity in women in such high-order cortical regions such as prefrontal cortex and superior parietal lobule, in accord with prior findings, and with the idea that women may take a more “top-down” approach to mental rotation. By quantifying, in a data-driven fashion, the contribution of factors such as sex and education to patterns of brain activity, these findings put the magnitude of neural sex differences during mental rotation into perspective, confirming the commonsense notion that, as humans, men and women are more alike than they are different, with between-individual variability (such as level of education, which, importantly, is modifiable) generally outweighing between-sex variability. This work exemplifies the role that multivariate analysis can play in identifying brain functional networks, and in quantifying their involvement under specific conditions and in different populations.