The brain as a crystal ball: the predictive potential of default mode network

Association between Gray and White Matter Lesions and Its Involvement in Clinical Symptoms of Alzheimer's-Type Dementia

BACKGROUND

Not only gray matter lesions (GMLs) but also white matter lesions (WMLs) can play important roles in the pathology of Alzheimer's disease (AD). The progression of cognitive impairment (CI) and behavioral and psychological symptoms of dementia (BPSD) might be caused by a concerted effect of both GML and WML.

OBJECTIVE

This study aimed to investigate the association between GML and WML and how they are involved in the symptoms of CI and BPSD in dementia patients by means of imaging technology.

METHODS

Patients in our memory clinic, who were diagnosed with AD-type dementia or amnestic mild cognitive impairment (aMCI) and had undergone both single-photon emission computed tomography (SPECT) and brain MRI, were consecutively enrolled (n = 156; 61 males and 95 females; 79.8 ± 7.4 years old). Symptoms of CI and BPSD were obtained from patients' medical records. For the analysis of GMLs and WMLs, SPECT data and MRI T1-weighted images were used, respectively. This study followed the Declaration of Helsinki, and all procedures were approved by the institutional ethics committee.

RESULTS

According to a multivariate analysis, disorientation and disturbed attention demonstrated a relationship between the precuneus and WMLs in both hemispheres. Hyperactivity in BPSD showed multiple correlations between GMLs on both sides of the frontal cortex and WMLs. Patients with aMCI presented more multiple correlations between GMLs and WMLs compared with those with AD-type dementia regarding dementia symptoms including BPSD.

CONCLUSION

The interaction between GMLs and WMLs may vary depending on the symptoms of CI and BPSD. Hyperactivity in BPSD may be affected by the functional relationship between GMLs and WMLs in the left and right hemispheres. The correlation between GMLs and WMLs may be changing in AD-type dementia and aMCI.

Impaired topographic organization in Parkinson's disease with mild cognitive impairment

BACKGROUND

Mild cognitive impairment (MCI) is common in Parkinson's disease (PD), and graph theory approaches can be performed to investigate the topographic organization in newly diagnosed drug-naïve PD patients with MCI.

METHOD

We recruited PD patients with MCI (PD-MCI), PD patients with cognitive unimpaired (PD-CU), and age- and sex-matched healthy controls (HCs). Resting-state functional MRI (fMRI) whole-brain connectivity was examined, and topographic properties were measured with age, sex and education as covariates. Correlation analyses were performed between topographic features and cognitive scores.

RESULTS

Newly diagnosed drug-naïve PD patients and HCs presented small-world properties, and PD patients had increasing random organizations of brain networks, especially in PD patients with MCI. We also found a descending trend (HC > PD-CU > PD-MCI) in the clustering coefficient (C_p), characteristic path length (L_p) and local efficiency (E_loc), and a rising trend (HC < PD-CU < PD-MCI) in the global efficiency (E_glob). Only PD patients with MCI showed decreased nodal centralities in nodes of the sensorimotor network (SMN), default mode network (DMN), and the ventral anterior prefrontal cortex (vent aPFC), and increased nodal centralities in nodes of the cingulo-opercular network (CON), occipital network, and the ventral lateral prefrontal cortex (vlPFC). The increased nodal centralities in the parietal node of CON negatively correlated with cognitive scores in all PD patients.

CONCLUSION

Our results suggested that newly diagnosed drug-naïve PD patients had increasing random organizations of brain networks, especially in PD-MCI patients. Nodal changes were mainly observed in PD-MCI patients.

Mechanisms of automaticity and anticipatory control in fluid intelligence

The constructs of fluid (Gf) and crystalized (Gc) intelligence represent an early attempt to describe the mechanisms of problem solving in the vertebrate brain. Modern neuroscience demonstrates that problem solving involves interplay between the mechanisms of automaticity and anticipatory control, enabling nature's elegant solution to the challenges animals face in their environment. Studies of neural functioning are making clear the primary role of cortical-subcortical interactions in the manifestation of intelligent behavior in humans and other vertebrates. A tridimensional model of intelligent problem solving is explored, wherein the basal ganglia system (BGS) and cerebrocerebellar system (CCS) interact within large scale brain networks. The BGS and CCS work together to enable automaticity to occur. The BGS enables the organism to learn what to do through a powerful instrumental learning system. The BGS also regulates when behavior is released through an inhibitory system which is incredibly sensitive to context. The CCS enables the organism to learn how to perform adaptive behaviors. Internal cerebellar models enable gradual improvements in the quality of behavioral output. The BGS and CCS interact within large scale brain networks, including the dorsal attention network (DAN), ventral attention network (VAN), default mode network (DMN) and frontoparietal network (FPN). The interactions of these systems enable vertebrate organisms to develop a vast array of complex adaptive behaviors. The benefits and importance of developing clinical tests to measure the integrity of these systems is considered.

Resting-state functional brain networks in Parkinson's disease

The network approach is increasingly being applied to the investigation of normal brain function and its impairment. In the present review, we introduce the main methodological approaches employed for the analysis of resting‐state neuroimaging data in Parkinson's disease studies. We then summarize the results of recent studies that used a functional network perspective to evaluate the changes underlying different manifestations of Parkinson's disease, with an emphasis on its cognitive symptoms. Despite the variability reported by many studies, these methods show promise as tools for shedding light on the pathophysiological substrates of different aspects of Parkinson's disease, as well as for differential diagnosis, treatment monitoring and establishment of imaging biomarkers for more severe clinical outcomes.

Structure and function of large-scale brain systems

This article introduces the functional neuroanatomy of large-scale brain systems. Both the structure and functions of these brain networks are presented. All human behavior is the result of interactions within and between these brain systems. This system of brain function completely changes our understanding of how cognition and behavior are organized within the brain, replacing the traditional lesion model. Understanding behavior within the context of brain network interactions has profound implications for modifying abstract constructs such as attention, learning, and memory. These constructs also must be understood within the framework of a paradigm shift, which emphasizes ongoing interactions within a dynamically changing environment.

Discriminative analysis of Parkinson's disease based on whole-brain functional connectivity

Recently, there has been an increasing emphasis on applications of pattern recognition and neuroimaging techniques in the effective and accurate diagnosis of psychiatric or neurological disorders. In the present study, we investigated the whole-brain resting-state functional connectivity patterns of Parkinson's disease (PD), which are expected to provide additional information for the clinical diagnosis and treatment of this disease. First, we computed the functional connectivity between each pair of 116 regions of interest derived from a prior atlas. The most discriminative features based on Kendall tau correlation coefficient were then selected. A support vector machine classifier was employed to classify 21 PD patients with 26 demographically matched healthy controls. This method achieved a classification accuracy of 93.62% using leave-one-out cross-validation, with a sensitivity of 90.47% and a specificity of 96.15%. The majority of the most discriminative functional connections were located within or across the default mode, cingulo-opercular and frontal-parietal networks and the cerebellum. These disease-related resting-state network alterations might play important roles in the pathophysiology of this disease. Our results suggest that analyses of whole-brain resting-state functional connectivity patterns have the potential to improve the clinical diagnosis and treatment evaluation of PD.

Large-scale brain systems and subcortical relationships: practical applications

This article describes an interactive paradigm for understanding brain functioning. This model requires both explicit and implicit learning processes. This paradigm is illustrated through the interpretation of practical examples of behavior. Applications of current neuropsychological tests are presented within this interactive paradigm. The development of new neuropsychological tests is presented, as derived from experimental test paradigms that evaluate implicit learning processes.

Brain functional connectivity changes in children that differ in impulsivity temperamental trait

Impulsivity is a core personality trait forming part of normal behavior and contributing to adaptive functioning. However, in typically developing children, altered patterns of impulsivity constitute a risk factor for the development of behavioral problems. Since both pathological and non-pathological states are commonly characterized by continuous transitions, we used a correlative approach to investigate the potential link between personality and brain dynamics. We related brain functional connectivity of typically developing children, measured with magnetic resonance imaging at rest, with their impulsivity scores obtained from a questionnaire completed by their parents. We first looked for areas within the default mode network (DMN) whose functional connectivity might be modulated by trait impulsivity. Then, we calculated the functional connectivity among these regions and the rest of the brain in order to assess if impulsivity trait altered their relationships. We found two DMN clusters located at the posterior cingulate cortex and the right angular gyrus which were negatively correlated with impulsivity scores. The whole-brain correlation analysis revealed the classic network of correlating and anti-correlating areas with respect to the DMN. The impulsivity trait modulated such pattern showing that the canonical anti-phasic relation between DMN and action-related network was reduced in high impulsive children. These results represent the first evidence that the impulsivity, measured as personality trait assessed through parents' report, exerts a modulatory influence over the functional connectivity of resting state brain networks in typically developing children. The present study goes further to connect developmental approaches, mainly based on data collected through the use of questionnaires, and behavioral neuroscience, interested in how differences in brain structure and functions reflect in differences in behavior.

Neural markers of errors as endophenotypes in neuropsychiatric disorders

Learning from errors is fundamental to adaptive human behavior. It requires detecting errors, evaluating what went wrong, and adjusting behavior accordingly. These dynamic adjustments are at the heart of behavioral flexibility and accumulating evidence suggests that deficient error processing contributes to maladaptively rigid and repetitive behavior in a range of neuropsychiatric disorders. Neuroimaging and electrophysiological studies reveal highly reliable neural markers of error processing. In this review, we evaluate the evidence that abnormalities in these neural markers can serve as sensitive endophenotypes of neuropsychiatric disorders. We describe the behavioral and neural hallmarks of error processing, their mediation by common genetic polymorphisms, and impairments in schizophrenia, obsessive-compulsive disorder, and autism spectrum disorders. We conclude that neural markers of errors meet several important criteria as endophenotypes including heritability, established neuroanatomical and neurochemical substrates, association with neuropsychiatric disorders, presence in syndromally-unaffected family members, and evidence of genetic mediation. Understanding the mechanisms of error processing deficits in neuropsychiatric disorders may provide novel neural and behavioral targets for treatment and sensitive surrogate markers of treatment response. Treating error processing deficits may improve functional outcome since error signals provide crucial information for flexible adaptation to changing environments. Given the dearth of effective interventions for cognitive deficits in neuropsychiatric disorders, this represents a potentially promising approach.

Specific and evolving resting-state network alterations in post-concussion syndrome following mild traumatic brain injury

Post-concussion syndrome has been related to axonal damage in patients with mild traumatic brain injury, but little is known about the consequences of injury on brain networks. In the present study, our aim was to characterize changes in functional brain networks following mild traumatic brain injury in patients with post-concussion syndrome using resting-state functional magnetic resonance imaging data. We investigated 17 injured patients with persistent post-concussion syndrome (under the DSM-IV criteria) at 6 months post-injury compared with 38 mild traumatic brain injury patients with no post-concussion syndrome and 34 healthy controls. All patients underwent magnetic resonance imaging examinations at the subacute (1-3 weeks) and late (6 months) phases after injury. Group-wise differences in functional brain networks were analyzed using graph theory measures. Patterns of long-range functional networks alterations were found in all mild traumatic brain injury patients. Mild traumatic brain injury patients with post-concussion syndrome had greater alterations than patients without post-concussion syndrome. In patients with post-concussion syndrome, changes specifically affected temporal and thalamic regions predominantly at the subacute stage and frontal regions at the late phase. Our results suggest that the post-concussion syndrome is associated with specific abnormalities in functional brain network that may contribute to explain deficits typically observed in PCS patients.

Weighing brain activity with the balance: Angelo Mosso's original manuscripts come to light

Neuroimaging techniques, such as positron emission tomography and functional magnetic resonance imaging are essential tools for the analysis of organized neural systems in working and resting states, both in physiological and pathological conditions. They provide evidence of coupled metabolic and cerebral local blood flow changes that strictly depend upon cellular activity. In 1890, Charles Smart Roy and Charles Scott Sherrington suggested a link between brain circulation and metabolism. In the same year William James, in his introduction of the concept of brain blood flow variations during mental activities, briefly reported the studies of the Italian physiologist Angelo Mosso, a multifaceted researcher interested in the human circulatory system. James focused on Mosso's recordings of brain pulsations in patients with skull breaches, and in the process only briefly referred to another invention of Mosso's, the 'human circulation balance', which could non-invasively measure the redistribution of blood during emotional and intellectual activity. However, the details and precise workings of this instrument and the experiments Mosso performed with it have remained largely unknown. Having found Mosso's original manuscripts in the archives, we remind the scientific community of his experiments with the 'human circulation balance' and of his establishment of the conceptual basis of non-invasive functional neuroimaging techniques. Mosso unearthed and investigated several critical variables that are still relevant in modern neuroimaging such as the 'signal-to-noise ratio', the appropriate choice of the experimental paradigm and the need for the simultaneous recording of differing physiological parameters.

Long-term effects of mild traumatic brain injury on cognitive performance

Although a proportion of individuals report chronic cognitive difficulties after mild traumatic brain injury (mTBI), results from behavioral testing have been inconsistent. In fact, the variability inherent to the mTBI population may be masking subtle cognitive deficits. We hypothesized that this variability could be reduced by accounting for post-concussion syndrome (PCS) in the sample. Thirty-six participants with mTBI (>1 year post-injury) and 36 non-head injured controls performed information processing speed (Paced Visual Serial Addition Task, PVSAT) and working memory (n-Back) tasks. Both groups were split by PCS diagnosis (4 groups, all n = 18), with categorization of controls based on symptom report. Participants with mTBI and persistent PCS had significantly greater error rates on both the n-Back and PVSAT, at every difficulty level except 0-Back (used as a test of performance validity). There was no difference between any of the other groups. Therefore, a cognitive deficit can be observed in mTBI participants, even 1 year after injury. Correlations between cognitive performance and symptoms were only observed for mTBI participants, with worse performance correlating with lower sleep quality, in addition to a medium effect size association (falling short of statistical significance) with higher PCS symptoms, post-traumatic stress disorder (PTSD), and anxiety. These results suggest that the reduction in cognitive performance is not due to greater symptom report itself, but is associated to some extent with the initial injury. Furthermore, the results validate the utility of our participant grouping, and demonstrate its potential to reduce the variability observed in previous studies.