Disconnecting cognition

Automated Classification of Mild Cognitive Impairment by Machine Learning With Hippocampus-Related White Matter Network

Background

Detection of mild cognitive impairment (MCI) is essential to screen high risk of Alzheimer’s disease (AD). However, subtle changes during MCI make it challenging to classify in machine learning. The previous pathological analysis pointed out that the hippocampus is the critical hub for the white matter (WM) network of MCI. Damage to the white matter pathways around the hippocampus is the main cause of memory decline in MCI. Therefore, it is vital to biologically extract features from the WM network driven by hippocampus-related regions to improve classification performance.

Methods

Our study proposes a method for feature extraction of the whole-brain WM network. First, 42 MCI and 54 normal control (NC) subjects were recruited using diffusion tensor imaging (DTI), resting-state functional magnetic resonance imaging (rs-fMRI), and T1-weighted (T1w) imaging. Second, mean diffusivity (MD) and fractional anisotropy (FA) were calculated from DTI, and the whole-brain WM networks were obtained. Third, regions of interest (ROIs) with significant functional connectivity to the hippocampus were selected for feature extraction, and the hippocampus (HIP)-related WM networks were obtained. Furthermore, the rank sum test with Bonferroni correction was used to retain significantly different connectivity between MCI and NC, and significant HIP-related WM networks were obtained. Finally, the classification performances of these three WM networks were compared to select the optimal feature and classifier.

Results

(1) For the features, the whole-brain WM network, HIP-related WM network, and significant HIP-related WM network are significantly improved in turn. Also, the accuracy of MD networks as features is better than FA. (2) For the classification algorithm, the support vector machine (SVM) classifier with radial basis function, taking the significant HIP-related WM network in MD as a feature, has the optimal classification performance (accuracy = 89.4%, AUC = 0.954). (3) For the pathologic mechanism, the hippocampus and thalamus are crucial hubs of the WM network for MCI.

Conclusion

Feature extraction from the WM network driven by hippocampus-related regions provides an effective method for the early diagnosis of AD.

Altered Regional Brain Homogeneity of BOLD Signal in CADASIL: A Resting State fMRI Study

BACKGROUND AND PURPOSE

The cognitive decline in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is assumed to be due to a cortical-subcortical disconnection secondary to damage to the cerebral white matter (WM). Using resting state functional MRI (rsfMRI) and analysis of the regional homogeneity (ReHo), we examined a group of CADASIL patients and a group of healthy subjects in order to: (1) explore possible differences between the two groups; and (2) to assess, in CADASIL patients, whether any ReHo abnormalities correlate with individual burdens of WM T₂ -weighted hyperintensity and diffusion tensor imaging (DTI)-derived index of mean diffusivity (MD) of the cerebral WM, an index reflecting microstructural damage in CADASIL.

METHODS

Twenty-three paucisymptomatic CADASIL patients (13 females; age mean ± standard deviation = 43.6 ± 11.1 years; three symptomatic and 20 with no or few symptoms) and 16 healthy controls (nine females; age 46.6 ± 11.0 years) were examined with T₁ -weighted, T₂ -weighted fluid attenuated inversion recovery images, DTI, and rsfMRI.

RESULTS

When compared to controls, CADASIL patients showed four clusters of significantly lower ReHo values in cortical areas belonging to networks involved in inhibition and attention, including the right insula, the left superior frontal gyrus, and the bilateral anterior cingulated cortex. ReHo changes did not correlate with an individual patient's lesion burden or MD.

CONCLUSIONS

This study reveals decreased ReHo of rsfMRI signals in cortical areas involved in inhibition and attention processes, suggesting a potential role for these functional cortical changes in CADASIL.

Postoperative delirium in the elderly: the potential neuropathogenesis

Postoperative delirium (POD) is a neurobehavioral syndrome caused by dysfunction of neural activity mainly in elderly people. POD is not uncommon, but under-recognized, and often serious. Multifactorial causes including aging, acetylcholine deficiency, sleep deprivation and intraoperative hypoxia have been proposed attempting to explain the processes leading to the development of POD. To date, however, no specific pathophysiologic mechanism has been identified. Here, we summarize the five most prominent theories (neuronal aging, neuroinflammation, neurotransmitter imbalance, neuroendocrine activation, and network connectivity change) to explain the development of delirium. Understanding of the neuropathogenesis of delirium will help focus future research, and assist in developing prophylactic and treatment strategies.

What does scalp electroencephalogram coherence tell us about long-range cortical networks?

Long-range interactions between cortical areas are undoubtedly a key to the computational power of the brain. For healthy human subjects, the premier method for measuring brain activity on fast timescales is electroencephalography (EEG), and coherence between EEG signals is often used to assay functional connectivity between different brain regions. However, the nature of the underlying brain activity that is reflected in EEG coherence is currently the realm of speculation, because seldom have EEG signals been recorded simultaneously with intracranial recordings near cell bodies in multiple brain areas. Here, we take the early steps towards narrowing this gap in our understanding of EEG coherence by measuring local field potentials with microelectrode arrays in two brain areas (extrastriate visual area V4 and dorsolateral prefrontal cortex) simultaneously with EEG at the nearby scalp in rhesus macaque monkeys. Although we found inter-area coherence at both scales of measurement, we did not find that scalp-level coherence was reliably related to coherence between brain areas measured intracranially on a trial-to-trial basis, despite that scalp-level EEG was related to other important features of neural oscillations, such as trial-to-trial variability in overall amplitudes. This suggests that caution must be exercised when interpreting EEG coherence effects, and new theories devised about what aspects of neural activity long-range coherence in the EEG reflects.

Agraphia of the left hand with dysfunction of the left superior parietal region without callosal lesions

A 28-year-old right-handed man noticed weakness in his legs, three days after an ephedrine overdose. Initial brain magnetic resonance imaging showed lesions in the parietal regions bilaterally. Computed tomography angiography showed segmental and multifocal vasoconstriction of the cerebral arteries. After treatment, clinical and radiological findings resolved, suggesting the patient had reversible cerebral vasoconstriction syndrome with posterior reversible encephalopathy syndrome. However, he had residual agraphia of the left hand. Language testing revealed no difficulties in oral expression, auditory comprehension, understanding of written language, or writing with the right hand. I-123 iodoamphetamine single-photon emission computed tomography showed residual dysfunction in the left superior parietal lobule. There were no apparent signs of other disconnection syndromes or neuroimaging abnormalities in the corpus callosum. We diagnosed left-hand agraphia due to left parietal dysfunction. Our case suggests that left superior parietal dysfunction without callosal lesions is a possible cause of left-hand agraphia. Neural mechanisms for writing with the right or left hand may be separable at the cortical level.

•

A patient had the RCVS with PRES after an ephedrine overdose.

•

The only residual neurological symptom was the left hand agraphia.

•

Neuroimaging study revealed selective dysfunction in the left parietal regions.

•

There were no apparent lesions detected in the corpus callosum.

•

Left hand agraphia is possible without callosal lesions.

Resting state connectivity and cognitive performance in adults with cerebral autosomal-dominant arteriopathy with subcortical infarcts and leukoencephalopathy

Cognitive impairment is an inevitable feature of cerebral autosomal-dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), affecting executive function, attention and processing speed from an early stage. Impairment is associated with structural markers such as lacunes, but associations with functional connectivity have not yet been reported. Twenty-two adults with genetically-confirmed CADASIL (11 male; aged 49.8 ± 11.2 years) underwent functional magnetic resonance imaging at rest. Intrinsic attentional/executive networks were identified using group independent components analysis. A linear regression model tested voxel-wise associations between cognitive measures and component spatial maps, and Pearson correlations were performed with mean intra-component connectivity z-scores. Two frontoparietal components were associated with cognitive performance. Voxel-wise analyses showed an association between one component cluster and processing speed (left middle temporal gyrus; peak -48, -18, -14; ZE = 5.65, pFWE corr = 0.001). Mean connectivity in both components correlated with processing speed (r = 0.45, p = 0.043; r = 0.56, p = 0.008). Mean connectivity in one component correlated with faster Trailmaking B minus A time (r = -0.77, p < 0.001) and better executive performance (r = 0.56, p = 0.011). This preliminary study provides evidence for associations between cognitive performance and attentional network connectivity in CADASIL. Functional connectivity may be a useful biomarker of cognitive performance in this population.

Structural white-matter connections mediating distinct behavioral components of spatial neglect in right brain-damaged patients

Spatial neglect is a neuropsychological syndrome in which patients fail to perceive and orient to stimuli located in the space contralateral to the lesioned hemisphere. It is characterized by a wide heterogeneity in clinical symptoms which can be grouped into distinct behavioral components correlating with different lesion sites. Moreover, damage to white-matter (WM) fiber tracts has been suggested to disconnect brain networks that mediate different functions associated with spatial cognition and attention. However, it remains unclear what WM pathways are associated with functionally dissociable neglect components. In this study we examined nine patients with a focal right hemisphere stroke using a series of neuropsychological tests and diffusion tensor imaging (DTI) in order to disentangle the role of specific WM pathways in neglect symptoms. First, following previous work, the behavioral test scores of patients were factorized into three independent components reflecting perceptual, exploratory, and object-centered deficits in spatial awareness. We then examined the structural neural substrates of these components by correlating indices of WM integrity (fractional anisotropy) with the severity of deficits along each profile. Several locations in the right parietal and frontal WM correlated with neuropsychological scores. Fiber tracts projecting from these locations indicated that posterior parts of the superior longitudinal fasciculus (SLF), as well as nearby callosal fibers connecting ipsilateral and contralateral parietal areas, were associated with perceptual spatial deficits, whereas more anterior parts of SLF and inferior fronto-occipital fasciculus (IFOF) were predominantly associated with object-centered deficits. In addition, connections between frontal areas and superior colliculus were found to be associated with the exploratory deficits. Our results provide novel support to the view that neglect may result from disconnection lesions in distributed brain networks, but also extend these notions by highlighting the role of dissociable circuits in different functional components of the neglect syndrome. However these preliminary findings require replication with larger samples of patients.

Hurt but still alive: Residual activity in the parahippocampal cortex conditions the recognition of familiar places in a patient with topographic agnosia

The parahippocampal cortex (PHC) participates in both perception and memory. However, the way perceptual and memory processes cooperate when we navigate in our everyday life environment remains poorly understood. We studied a stroke patient presenting a brain lesion in the right PHC, which resulted in a mild and quantifiable topographic agnosia, and allowed us to investigate the role of this structure in overt place recognition. Photographs of personally familiar and unfamiliar places were displayed during functional magnetic resonance imaging (fMRI). Familiar places were either recognized or unrecognized by the patient and 6 age- and education-matched controls in a visual post-scan recognition test. In fMRI, recognized places were associated with a network comprising the fusiform gyrus in the intact side, but also the right anterior PHC, which included the lesion site. Moreover, this right PHC showed increased connectivity with the left homologous PHC in the intact hemisphere. By contrasting recognized with unrecognized familiar places, we replicate the finding of the joint involvement of the retrosplenial cortex, occipito-temporal areas, and posterior parietal cortex in place recognition. This study shows that the ability for left and right anterior PHC to communicate despite the neurological damage conditioned place recognition success in this patient. It further highlights a hemispheric asymmetry in this process, by showing the fundamental role of the right PHC in topographic agnosia.

Multiscale Enaction Model (MEM): the case of complexity and "context-sensitivity" in vision

I review the data on human visual perception that reveal the critical role played by non-visual contextual factors influencing visual activity. The global perspective that progressively emerges reveals that vision is sensitive to multiple couplings with other systems whose nature and levels of abstraction in science are highly variable. Contrary to some views where vision is immersed in modular hard-wired modules, rather independent from higher-level or other non-cognitive processes, converging data gathered in this article suggest that visual perception can be theorized in the larger context of biological, physical, and social systems with which it is coupled, and through which it is enacted. Therefore, any attempt to model complexity and multiscale couplings, or to develop a complex synthesis in the fields of mind, brain, and behavior, shall involve a systematic empirical study of both connectedness between systems or subsystems, and the embodied, multiscale and flexible teleology of subsystems. The conceptual model (Multiscale Enaction Model [MEM]) that is introduced in this paper finally relates empirical evidence gathered from psychology to biocomputational data concerning the human brain. Both psychological and biocomputational descriptions of MEM are proposed in order to help fill in the gap between scales of scientific analysis and to provide an account for both the autopoiesis-driven search for information, and emerging perception.

Where numbers meet words: a common ventral network for semantic classification

Recent research has shown that both language and number processing are clear examples of distributed and connected processing in the human brain, emphasizing the importance of white matter connections between the associated cortex sites. Against this background we hypothesized joint cognitive processes and functions in a cross-domain manner to be reflected by the involvement of specific white matter tracts. Therefore, we evaluated white matter connectivity for the specific cognitive process of semantic classification, which is an integral part of tasks commonly employed to investigate the neural correlates of language and number processing. In line with our expectations, fiber tracking results clearly indicated a common ventral network for semantic classification for the domains of language and number processing. Thereby, the present data are hard to reconcile with a localizationalist view on processing characteristics of the human brain, but strongly suggest that white matter connectivity should be considered when investigating the neural underpinnings of human cognition.