Recent studies revealed a positive influence of physical activity on cognitive functioning in older adults. Studies that investigate the behavioral and neurophysiological effects of type and long term duration of physical training, however, are missing. We performed a 12-month longitudinal study to investigate the effects of cardiovascular and coordination training (control group: relaxation and stretching) on cognitive functions (executive control and perceptual speed) in older adults. We analyzed data of 44 participants aged 62-79 years. Participants were trained three times a week for 12 months. Their physical and cognitive performance was tested prior to training, and after 6 and 12 months. Changes in brain activation patterns were investigated using functional MRI. On the behavioral level, both experimental groups improved in executive functioning and perceptual speed but with differential effects on speed and accuracy. In line with the behavioral findings, neurophysiological results for executive control also revealed changes (increases and reductions) in brain activity for both interventions in frontal, parietal, and sensorimotor cortical areas. In contrast to the behavioral findings, neurophysiological changes were linear without indication of a plateau. In both intervention groups, prefrontal areas showed decreased activation after 6 and 12 months when performing an executive control task, as compared to the control group, indicating more efficient information processing. Furthermore, cardiovascular training was associated with an increased activation of the sensorimotor network, whereas coordination training was associated with increased activation in the visual-spatial network. Our data suggest that besides cardiovascular training also other types of physical activity improve cognition of older adults. The mechanisms, however, that underlie the performance changes seem to differ depending on the intervention.
Computational learning models are critical for understanding mechanisms of adaptive behavior. However, the two major current frameworks, reinforcement learning (RL) and Bayesian learning, both have certain limitations. For example, many Bayesian models are agnostic of inter-individual variability and involve complicated integrals, making online learning difficult. Here, we introduce a generic hierarchical Bayesian framework for individual learning under multiple forms of uncertainty (e. g., environmental volatility and perceptual uncertainty). The model assumes Gaussian random walks of states at all but the first level, with the step size determined by the next highest level. The coupling between levels is controlled by parameters that shape the influence of uncertainty on learning in a subject-specific fashion. Using variational Bayes under a mean-field approximation and a novel approximation to the posterior energy function, we derive trial-by-trial update equations which (i) are analytical and extremely efficient, enabling real-time learning, (ii) have a natural interpretation in terms of RL, and (iii) contain parameters representing processes which play a key role in current theories of learning, e. g., precision-weighting of prediction error. These parameters allow for the expression of individual differences in learning and may relate to specific neuromodulatory mechanisms in the brain. Our model is very general: it can deal with both discrete and continuous states and equally accounts for deterministic and probabilistic relations between environmental events and perceptual states (i. e., situations with and without perceptual uncertainty). These properties are illustrated by simulations and analyses of empirical time series. Overall, our framework provides a novel foundation for understanding normal and pathological learning that contextualizes RL within a generic Bayesian scheme and thus connects it to principles of optimality from probability theory.
Spoken language exists because of a remarkable neural process. Inside a speaker's brain, an intended message gives rise to neural signals activating the muscles of the vocal tract. The process is remarkable because these muscles are activated in just the right way that the vocal tract produces sounds a listener understands as the intended message. What is the best approach to understanding the neural substrate of this crucial motor control process? One of the key recent modeling developments in neuroscience has been the use of state feedback control (SFC) theory to explain the role of the CNS in motor control. SFC postulates that the CNS controls motor output by (1) estimating the current dynamic state of the thing (e. g., arm) being controlled, and (2) generating controls based on this estimated state. SFC has successfully predicted a great range of non-speech motor phenomena, but as yet has not received attention in the speech motor control community. Here, we review some of the key characteristics of speech motor control and what they say about the role of the CNS in the process. We then discuss prior efforts to model the role of CNS in speech motor control, and argue that these models have inherent limitations - limitations that are overcome by an SFC model of speech motor control which we describe. We conclude by discussing a plausible neural substrate of our model.
Recent neuroimaging work has demonstrated that the hippocampus is engaged when imagining the future, in some cases more than when remembering the past. It is possible that this hippocampal activation reflects recombining details into coherent scenarios and/or the encoding of these scenarios into memory for later use. However, inconsistent findings have emerged from recent studies of future simulation in patients with memory loss and hippocampal damage. Thus, it remains an open question as to whether the hippocampus is necessary for future simulation. In this review, we consider the findings from patient studies and the neuroimaging literature with respect to a new framework that high-lights three component processes of simulation: accessing episodic details, recombining details, and encoding simulations. We attempt to reconcile these discrepancies between neuroimaging and patient studies by suggesting that different component processes of future simulation may be differentially affected by hippocampal damage.
Although the adult brain was once seen as a rather static organ, it is now clear that the organization of brain circuitry is constantly changing as a function of experience or learning. Yet, research also shows that learning is often specific to the trained stimuli and task, and does not improve performance on novel tasks, even very similar ones. This perspective examines the idea that systematic mental training, as cultivated by meditation, can induce learning that is not stimulus or task specific, but process specific. Many meditation practices are explicitly designed to enhance specific, well-defined core cognitive processes. We will argue that this focus on enhancing core cognitive processes, as well as several general characteristics of meditation regimens, may specifically foster process-specific learning. To this end, we first define meditation and discuss key findings from recent neuroimaging studies of meditation. We then identify several characteristics of specific meditation training regimes that may determine process-specific learning. These characteristics include ongoing variability in stimulus input, the meta-cognitive nature of the processes trained, task difficulty, the focus on maintaining an optimal level of arousal, and the duration of training. Lastly, we discuss the methodological challenges that researchers face when attempting to control or characterize the multiple factors that may underlie meditation training effects.
The year 2009 marked the 100th anniversary of the publication of the famous brain map of Korbinian Brodmann. Although a "classic" guide to microanatomical parcellation of the cerebral cortex, it is - from today's state-of-the-art neuroimaging perspective - problematic to use Brodmann's map as a structural guide to functional units in the cortex. In this article we discuss some of the reasons, especially the problematic compatibility of the "post-mortem world" of microstructural brain maps with the "in vivo world" of neuroimaging. We conclude with some prospects for the future of in vivo structural brain mapping: a new approach which has the enormous potential to make direct correlations between microstructure and function in living human brains: "in vivo Brodmann mapping" with high-field magnetic resonance imaging.
Until recently, it has been thought that under interocular suppression high-level visual processing is strongly inhibited if not abolished. With the development of continuous flash suppression (CFS), a variant of binocular rivalry, this notion has now been challenged by a number of reports showing that even high-level aspects of visual stimuli, such as familiarity, affect the time stimuli need to overcome CFS and emerge into awareness. In this "breaking continuous flash suppression" (b-CFS) paradigm, differential unconscious processing during suppression is inferred when (a) speeded detection responses to initially invisible stimuli differ, and (b) no comparable differences are found in non-rivalrous control conditions supposed to measure non-specific threshold differences between stimuli. The aim of the present study was to critically evaluate these assumptions. In six experiments we compared the detection of upright and inverted faces. We found that not only under CFS, but also in control conditions upright faces were detected faster and more accurately than inverted faces, although the effect was larger during CFS. However, reaction time (RT) distributions indicated critical differences between the CFS and the control condition. When RT distributions were matched, similar effect sizes were obtained in both conditions. Moreover, subjective ratings revealed that CFS and control conditions are not perceptually comparable. These findings cast doubt on the usefulness of non-rivalrous control conditions to rule out non-specific threshold differences as a cause of shorter detection latencies during CFS. Thus, at least in its present form, the b-CFS paradigm cannot provide unequivocal evidence for unconscious processing under interocular suppression. Nevertheless, our findings also demonstrate that the b-CFS paradigm can be fruitfully applied as a highly sensitive device to probe differences between stimuli in their potency to gain access to awareness.
Executive functioning deficits due to brain disease affecting frontal lobe functions cause significant real-life disability, yet solid evidence in support of executive functioning interventions is lacking. Goal Management Training (GMT), an executive functioning intervention that draws upon theories concerning goal processing and sustained attention, has received empirical support in studies of patients with traumatic brain injury, normal aging, and case studies. GMT promotes a mindful approach to complex real-life tasks that pose problems for patients with executive functioning deficits, with a main goal of periodically stopping ongoing behavior to monitor and adjust goals. In this controlled trial, an expanded version of GMT was compared to an alternative intervention, Brain Health Workshop that was matched to GMT on non-specific characteristics that can affect intervention outcome. Participants included 19 individuals in the chronic phase of recovery from brain disease (predominantly stroke) affecting frontal lobe function. Outcome data indicated specific effects of GMT on the Sustained Attention to Response Task as well as the Tower Test, a visuospatial problem-solving measure that reflected far transfer of training effects. There were no significant effects on self-report questionnaires, likely owing to the complexity of these measures in this heterogeneous patient sample. Overall, these data support the efficacy of GMT in the rehabilitation of executive functioning deficits.
In a longitudinal study of recovery of left neglect following stroke using reaction time computerized assessment, we find that lateralized spatial deficits of attention and perception to be more severe than disturbance of action. Perceptual-attention deficits also show the most variability in the course of recovery, making them prime candidates for intervention. In an anatomical analysis of MRI findings, ventral frontal cortex damage was correlated with the most severe neglect, reflecting impaired fronto-parietal communication.
Functional magnetic resonance imaging (fMRI) is one of the most widely used tools to study the neural underpinnings of human cognition. Standard analysis of fMRI data relies on a general linear model (GLM) approach to separate stimulus induced signals from noise. Crucially, this approach relies on a number of assumptions about the data which, for inferences to be valid, must be met. The current paper reviews the GLM approach to analysis of fMRI time-series, focusing in particular on the degree to which such data abides by the assumptions of the GLM framework, and on the methods that have been developed to correct for any violation of those assumptions. Rather than biasing estimates of effect size, the major consequence of nonconformity to the assumptions is to introduce bias into estimates of the variance, thus affecting test statistics, power, and false positive rates. Furthermore, this bias can have pervasive effects on both individual subject and group-level statistics, potentially yielding qualitatively different results across replications, especially after the thresholding procedures commonly used for inference-making.
Studies on developmental dyscalculia (DD) have tried to identify a basic numerical deficit that could account for this specific learning disability. The first proposition was that the number magnitude representation of these children was impaired. However, Rousselle and Noel (2007) brought data showing that this was not the case but rather that these children were impaired when processing the magnitude of symbolic numbers only. Since then, incongruent results have been published. In this paper, we will propose a developmental perspective on this issue. We will argue that the first deficit shown in DD regards the building of an exact representation of numerical value, thanks to the learning of symbolic numbers, and that the reduced acuity of the approximate number magnitude system appears only later and is secondary to the first deficit.
Recognizing emotion is an evolutionary imperative. An early stage of auditory scene analysis involves the perceptual grouping of acoustic features, which can be based on both temporal coincidence and spectral features such as perceived pitch. Perceived pitch, or fundamental frequency (F-0), is an especially salient cue for differentiating affective intent through speech intonation (prosody). We hypothesized that: (1) simple frequency-modulated tone abstractions, based on the parameters of actual prosodic stimuli, would be reliably classified as representing differing emotional categories; and (2) that such differences would yield significant mismatch negativities (MMNs) - an index of pre-attentive deviance detection within the auditory environment. We constructed a set of FM tones that approximated the F-0 mean and variation of reliably recognized happy and neutral prosodic stimuli. These stimuli were presented to 13 subjects using a passive listening oddball paradigm. We additionally included stimuli with no frequency modulation (FM) and FM tones with identical carrier frequencies but differing modulation depths as control conditions. Following electrophysiological recording, subjects were asked to identify the sounds they heard as happy, sad, angry, or neutral. We observed that FM tones abstracted from happy and no-expression speech stimuli elicited MMNs. Post hoc behavioral testing revealed that subjects reliably identified the FM tones in a consistent manner. Finally, we also observed that FM tones and no-FM tones elicited equivalent MMNs. MMNs to FM tones that differentiate affect suggests that these abstractions may be sufficient to characterize prosodic distinctions, and that these distinctions can be represented in pre-attentive auditory sensory memory.
Several studies have reported deficits in gamma oscillatory activity elicited by sensory stimulation or cognitive processes in schizophrenia patients (SZ) compared to healthy control subjects (HC). However, the evidence for cortical hyperexcitability and reduced function of N methyl-D-aspartate receptors (NMDARs) on parvalbumin-expressing inhibitory interneurons in schizophrenia leads to the prediction that gamma activity should rather be increased in SZ, but data supporting this hypothesis have been lacking. One possibility is that baseline induced gamma power is increased, an effect that might have gone unnoticed in studies of stimulus-locked oscillations. Here we addressed this question by re-analyzing the data from a previously published study on the 40 Hz auditory steady-state response (ASSR) in schizophrenia in which dipole source localization was used to examine gamma responses in the left and right auditory cortices. Subjects were 16 HC and 18 chronic SZ, who listened to trains of clicks presented at 40 Hz during electroencephalogram recording. Independent component analysis was used to remove ocular artifacts. Power spectra were computed for the prestimulus baseline period. We found that baseline power was higher in SZ than HC at 40 Hz in the left auditory cortex. Baseline 40 Hz power in the left auditory cortex was also correlated with ASSR evoked power in SZ. Thus, gamma oscillation abnormalities in schizophrenia may include abnormal increases in baseline power as well as deficits in evoked oscillations. These baseline increases could be the sign of NMDAR hypofunction on parvalbumin-expressing inhibitory interneurons, which would be consistent with acute NMDAR antagonism and genetic ablation models of schizophrenia.
Abnormal brain activity dynamics, in the sense of a thalamocortical dysrhythmia (TCD), has been proposed as the underlying mechanism for a subset of disorders that bridge the traditional delineations of neurology and neuropsychiatry. In order to test this proposal from a psychiatric perspective, a study using magnetoencephalography (MEG) was implemented in subjects with schizophrenic spectrum disorder (n = 14), obsessive-compulsive disorder (n = 10), or depressive disorder (n = 5) and in control individuals (n = 18). Detailed CNS electrophysiological analysis of these patients, using MEG, revealed the presence of abnormal theta range spectral power with typical TCD characteristics, in all cases. The use of independent component analysis and minimum-norm-based methods localized such TCD to ventromedial prefrontal and temporal cortices. The observed mode of oscillation was spectrally equivalent but spatially distinct from that of TCD observed in other related disorders, including Parkinson's disease, central tinnitus, neuropathic pain, and autism. The present results indicate that the functional basis for much of these pathologies may relate most fundamentally to the category of calcium channelopathies and serve as a model for the cellular substrate for low-frequency oscillations present in these psychiatric disorders, providing a basis for therapeutic strategies.
Gender differences in the regulation of body-weight are well documented. Here, we assessed obesity-related influences of gender on brain structure as well as performance in the Iowa Gambling Task. This task requires evaluation of both immediate rewards and long-term outcomes and thus mirrors the trade-off between immediate reward from eating and the long-term effect of overeating on body-weight. In women, but not in men, we show that the preference for salient immediate rewards in the face of negative long-term consequences is higher in obese than in lean subjects. In addition, we report structural differences in the left dorsal striatum (i.e., putamen) and right dorsolateral prefrontal cortex for women only. Functionally, both regions are known to play complimentary roles in habitual and goal-directed control of behavior in motivational contexts. For women as well as men, gray matter volume correlates positively with measures of obesity in regions coding the value and saliency of food (i.e., nucleus accumbens, orbitofrontal cortex) as well as in the hypothalamus (i.e., the brain's central homeostatic center). These differences between lean and obese subjects in hedonic and homeostatic control systems may reflect a bias in eating behavior toward energy-intake exceeding the actual homeostatic demand. Although we cannot infer from our results the etiology of the observed structural differences, our results resemble neural and behavioral differences well known from other forms of addiction, however, with marked differences between women and men. These findings are important for designing gender-appropriate treatments of obesity and possibly its recognition as a form of addiction.
The study of the brain as a whole system can be accomplished using network theory principles. Research has shown that human functional brain networks during a resting state exhibit small-world properties and high degree nodes, or hubs, localized to brain areas consistent with the default mode network. However, the study of brain networks across different tasks and or cognitive states has been inconclusive. Research in this field is important because the underpinnings of behavioral output are inherently dependent on whether or not brain networks are dynamic. This is the first comprehensive study to evaluate multiple network metrics at a voxel-wise resolution in the human brain at both the whole-brain and regional level under various conditions: resting state, visual stimulation, and multisensory (auditory and visual stimulation). Our results show that despite global network stability, functional brain networks exhibit considerable task-induced changes in connectivity, efficiency, and community structure at the regional level.
Mind-wandering (MW) is among the most robust and permanent expressions of human conscious awareness, classically regarded by philosophers, clinicians, and scientists as a core element of an intact sense of self. Nevertheless, the scientific exploration of MW poses unique challenges; MW is by nature a spontaneous, off task, internal mental process which is often unaware and usually difficult to control, document or replicate. Consequently, there is a lack of accepted modus operandi for exploring MW in a laboratory setup, leading to a relatively small amount of studies regarding the neural basis of MW. In order to facilitate scientific examination of MW the current review categorizes recent literature into five suggested strategies. Each strategy represents a different methodology of MW research within functional neuroimaging paradigms. Particular attention is paid to resting-state brain activity and to the "default-mode" network. Since the default network is known to exert high activity levels during off-task conditions, it stands out as a compelling candidate for a neuro-biological account of mind-wandering, in itself a rest-based phenomenon. By summarizing the results within and across strategies we suggest further insights into the neural basis and adaptive value of MW, a truly intriguing and unique human experience.
In the study of prosopagnosia, several issues (such as the specific or non-specific manifestations of prosopagnosia, the unitary or non-unitary nature of this syndrome and the mechanisms underlying face recognition disorders) are still controversial. Two main sources of variance partially accounting for these controversies could be the qualitative differences between the face recognition disorders observed in patients with prevalent lesions of the right or left hemisphere and in those with lesions encroaching upon the temporo-occipital (TO) or the (right) anterior temporal cortex. Results of our review seem to confirm these suggestions. Indeed, they show that (a) the most specific forms of prosopagnosia are due to lesions of a right posterior network including the occipital face area and the fusiform face area, whereas (b) the face identification defects observed in patients with left TO lesions seem due to a semantic defect impeding access to person-specific semantic information from the visual modality. Furthermore, face recognition defects resulting from right anterior temporal lesions can usually be considered as part of a multimodal people recognition disorder. The implications of our review are, therefore, the following: (1) to consider the components of visual agnosia often observed in prosopagnosic patients with bilateral TO lesions as part of a semantic defect, resulting from left-sided lesions (and not from prosopagnosia proper); (2) to systematically investigate voice recognition disorders in patients with right anterior temporal lesions to determine whether the face recognition defect should be considered a form of "associative prosopagnosia" or a form of the "multimodal people recognition disorder."
Goal-directed behavior requires the flexible transformation of sensory evidence about our environment into motor actions. Studies of perceptual decision-making have shown that this transformation is distributed across several widely separated brain regions. Yet, little is known about how decision-making emerges from the dynamic interactions among these regions. Here, we review a series of studies, in which we characterized the cortical network interactions underlying a perceptual decision process in the human brain. We used magnetoencephalography to measure the large-scale cortical population dynamics underlying each of the sub-processes involved in this decision: the encoding of sensory evidence and action plan, the mapping between the two, and the attentional selection of task-relevant evidence. We found that these sub-processes are mediated by neuronal oscillations within specific frequency ranges. Localized gamma-band oscillations in sensory and motor cortices reflect the encoding of the sensory evidence and motor plan. Large-scale oscillations across widespread cortical networks mediate the integrative processes connecting these local networks: Gamma- and beta-band oscillations across frontal, parietal, and sensory cortices serve the selection of relevant sensory evidence and its flexible mapping onto action plans. In sum, our results suggest that perceptual decisions are mediated by oscillatory interactions within overlapping local and large-scale cortical networks.
Studies of human adults, infants, and non-human animals demonstrate that non-symbolic numerical cognition is supported by atleast two distinct cognitive systems: a "parallel individuation system" that encodes the numerical identity of individual items and an "approximate number system" that encodes the approximate numerical magnitude, or numerosity, of a set. The exact nature and role of these systems, however, have been debated for over a 100-years. Some argue that the non-symbolic representation of small numbers (4) is carried out solely by the approximate number system. Others argue that all numbers are represented by the approximate number system. This debate has been fueled largely by some studies showing dissociations between small and large number processing and other studies showing similar processing of small and large numbers. Recent work has addressed this debate by showing that the two systems are present and distinct from early infancy, persist despite the acquisition of a symbolic number system, activate distinct cortical networks, and engage differentially based attentional constraints. Based on the recent discoveries, I provide a hypothesis that may explain the puzzling findings and makes testable predictions as to when each system will be engaged. In particular, when items are presented under conditions that allows election of individuals, they will be represented as distinct mental items through parallel individuation and not as a numerical magnitude. In contrast, when items are presented outside attentional limits(e.g., too many, too close together, under high attentional load), they will be represented as a single mental numerical magnitude and not as distinct mental items. These predictions provide a basis on which researchers can further investigate the role of each system in the development of uniquely human numerical thought.