RATIONALE: As the population of the world ages, age-related cognitive decline is becoming an ever-increasing problem. However, the changes in brain structure that accompany normal aging, and the role they play in cognitive decline, remain to be fully elucidated. AIMS: This study aims to characterize changes in brain structure in old age, and to investigate relationships between brain aging and cognitive decline using the Lothian Birth Cohort 1936. Here, we report the rationale, design and methodology of the brain and neurovascular imaging protocol developed to study this cohort. DESIGN: An observational, longitudinal study of the Lothian Birth Cohort 1936, which comprises 1091 relatively healthy individuals now in their 70s and living in the Edinburgh area. They are surviving participants of the Scottish Mental Survey 1947, which involved a test of general intelligence taken at age 11 years. At age 70 years, the Lothian Birth Cohort 1936 undertook detailed cognitive, medical and genetic testing, and provided social, family, nutritional, quality of life and physical activity information. At mean age 73 years they underwent detailed brain MRI and neurovascular ultrasound imaging, repeat cognitive and other testing. The MRI protocol is designed to provide qualitative and quantitative measures of gray and white matter atrophy, severity and location of white matter lesions, enlarged perivascular spaces, brain mineral deposits, microbleeds and integrity of major white matter tracts. The neurovascular ultrasound imaging provides velocity, stenosis and intima-media thickness measurements of the carotid and vertebral arteries. STUDY: This valuable imaging dataset will be used to determine which changes in brain structural parameters have the largest effects on cognitive aging. Analysis will include multimodal image analysis and multivariate techniques, such as factor analysis and structural equation modelling. Especially valuable is the ability within this sample to examine the influence that early life intelligence has on brain structural parameters in old age, and the role of genetic, vascular, educational and lifestyle factors. OUTCOMES: Final outcomes include associations between early and late life cognition and integrity of key white matter tracts, volume of gray and white matter, myelination, brain water content, and visible abnormalities such as white matter lesions and mineral deposits; and influences of vascular risk factors, diet, environment, social metrics, education and genetics on healthy brain aging. It is intended that this information will help to inform and develop strategies for successful cognitive aging.
Cognitive neuroscience explores the mechanisms of cognition by studying its structural and functional brain correlates. Here, we report the first systematic review that assesses how information from structural and functional neuroimaging methods can be integrated to investigate the brain substrates of cognition. Web of Science and Scopus databases were searched for studies of healthy young adult populations that collected cognitive data, and structural and functional neuroimaging data. Five percent of screened studies met all inclusion criteria. Next, 54% of included studies related cognitive performance to brain structure and function without quantitative analysis of the relationship. Finally, 32% of studies formally integrated structural and functional brain data. Overall, many studies consider either structural or functional neural correlates of cognition, and of those that consider both, they have rarely been integrated. We identified four emergent approaches to the characterisation of the relationship between brain structure, function and cognition; comparative, predictive, fusion and complementary. We discuss the insights provided each approach and how authors can select appr
Background: Adolescence is a critical period of brain maturation and heightened vulnerability to cognitive and mental health disorders. Sleep plays a vital role in neurodevelopment, yet the mechanisms linking insufficient sleep to adverse brain and behavioral outcomes remain unclear. The glymphatic system (GS), a brain-wide clearance pathway, may provide a key mechanistic link. Methods: Participants from the Adolescent Brain Cognitive Development (ABCD) Study (n =6,800; age ~ 11 years) were categorized into sleep-sufficient (>=9 h/night) and sleep-insufficient (<9 h/night) groups. Linear models tested associations among sleep, PVS burden, brain volumes, and behavioral outcomes. Mediation analyses evaluated whether PVS burden explained sleep-related effects. Results: Adolescents with insufficient sleep exhibited significantly greater PVS burden, reduced cortical, subcortical, and white matter volumes, poorer cognitive performance across multiple domains (largest effect in crystallized intelligence), and elevated psychopathology (largest effect in general problems). Sleep duration and quality were strongly associated with PVS burden. Mediation analyses revealed that PVS burden
Brain rhythms seem central to understanding the neurophysiological basis of human cognition. Yet, despite significant advances, key questions remain unresolved. In this comprehensive position paper, we review the current state of the art on oscillatory mechanisms and their cognitive relevance. The paper critically examines physiological underpinnings, from phase-related dynamics like cyclic excitability, to amplitude-based phenomena, such as gating by inhibition, and their interactions, such as phase-amplitude coupling, as well as frequency dynamics, like sampling mechanisms. We also critically evaluate future research directions, including travelling waves and brain-body interactions. We then provide an in-depth analysis of the role of brain rhythms across cognitive domains, including perception, attention, memory, and communication, emphasising ongoing debates and open questions in each area. By summarising current theories and highlighting gaps, this position paper offers a roadmap for future research, aimed at facilitating a unified framework of rhythmic brain function underlying cognition.
IMPORTANCE: β-Amyloid (Aβ) deposition and vascular brain injury (VBI) frequently co-occur and are both associated with cognitive decline in aging. Determining whether a direct relationship exists between them has been challenging. We sought to understand VBI's influence on cognition and clinical impairment, separate from and in conjunction with pathologic changes associated with Alzheimer disease (AD). OBJECTIVE: To examine the relationship between neuroimaging measures of VBI and brain Aβ deposition and their associations with cognition. DESIGN AND SETTING: A cross-sectional study in a community- and clinic-based sample recruited for elevated vascular disease risk factors. PARTICIPANTS: Clinically normal (mean age, 77.1 years [N = 30]), cognitively impaired (mean age, 78.0 years [N = 24]), and mildly demented (mean age, 79.8 years [N = 7]) participants. INTERVENTIONS: Magnetic resonance imaging, Aβ (Pittsburgh Compound B-positron emission tomographic [PiB-PET]) imaging, and cognitive testing. MAIN OUTCOME MEASURES: Magnetic resonance images were rated for the presence and location of infarct (34 infarct-positive participants, 27 infarct-negative participants) and were used to quantify white matter lesion volume. The PiB-PET uptake ratios were used to create a PiB index by averaging uptake across regions vulnerable to early Aβ deposition; PiB positivity (29 PiB-positive participants, 32 PiB-negative participants) was determined from a data-derived threshold. Standardized composite cognitive measures included executive function and verbal and nonverbal memory. RESULTS: Vascular brain injury and Aβ were independent in both cognitively normal and impaired participants. Infarction, particularly in cortical and subcortical gray matter, was associated with lower cognitive performance in all domains (P < .05 for all comparisons). Pittsburgh Compound B positivity was neither a significant predictor of cognition nor interacted with VBI. CONCLUSIONS AND RELEVANCE: In this elderly sample with normal cognition to mild dementia, enriched for vascular disease, VBI was more influential than Aβ in contemporaneous cognitive function and remained predictive after including the possible influence of Aβ. There was no evidence that VBI increases the likelihood of Aβ deposition. This finding highlights the importance of VBI in mild cognitive impairment and suggests that the impact of cerebrovascular disease should be considered with respect to defining the etiology of mild cognitive impairment.
A critical feature of the human brain that gives rise to complex cognition is its ability to reconfigure its network structure dynamically and adaptively in response to the environment. Existing research probing task-related reconfiguration of brain network structure has concluded that, although there are many similarities in network structure during an intrinsic, resting state and during the performance of a variety of cognitive tasks, there are meaningful differences as well. In this study, we related intrinsic, resting state network organization to reconfigured network organization during the performance of two tasks: a sequence tapping task, which is thought to probe motor execution and likely engages a single brain network, and an n-back task, which is thought to probe working memory and likely requires coordination across multiple networks. We implemented graph theoretical analyses using functional connectivity data from fMRI scans to calculate whole-brain measures of network organization in healthy young adults. We focused on quantifying measures of network segregation (modularity, system segregation, local efficiency, number of provincial hub nodes) and measures of network integration (global efficiency, number of connector hub nodes). Using these measures, we found converging evidence that local, within-network communication is critical for motor execution, whereas integrative, between-network communication is critical for working memory. These results confirm that the human brain has the remarkable ability to reconfigure its large-scale organization dynamically in response to current cognitive demands and that interpreting reconfiguration in terms of network segregation and integration may shed light on the optimal network structures underlying successful cognition. SIGNIFICANCE STATEMENT: The dynamic nature of the human brain gives rise to the wide range of behaviors and cognition of which humans are capable. We collected fMRI data from healthy young adults and measured large-scale functional connectivity patterns between regions distributed across the entire brain. We implemented graph theoretical analyses to quantify network organization during two tasks hypothesized to require different combinations of brain networks. During motor execution, segregation of distinct networks increased. Conversely, during working memory, integration across networks increased. These changes in network organization were related to better behavioral performance. These results underscore the human brain's ability to reconfigure network organization selectively and adaptively when confronted with changing cognitive demands to achieve an optimal balance between segregation and integration.
Metabolic syndrome (MetS), a clustering of risk factors for type 2 diabetes mellitus and cardiovascular disease, has been associated with cognitive dysfunction and brain abnormalities. This review describes the literature on the impact of MetS on brain and cognition and suggests directions for future research. A literature search for reports of MetS and cognition and brain imaging was conducted for both nonelderly adults and adolescents. No studies were found describing MetS and brain or cognition among adolescents; therefore, we also included studies investigating individual components of MetS in this age group. Most studies found associations between MetS and cognitive dysfunction. Multiple cognitive domains were affected by MetS in adults. In adolescents, the majority of findings were in executive functioning. Brain imaging literature in adults implicated MetS in ischemic stroke, white matter alterations, and altered brain metabolism. For adolescents, individual MetS factors were linked to volume losses in the hippocampus and frontal lobes. MetS negatively impacts cognitive performance and brain structure. Potential explanatory models include impaired vascular reactivity, neuroinflammation, oxidative stress, and abnormal brain lipid metabolism. We posit that insulin resistance-associated impairment in cerebrovascular reactivity is an important mechanism underlying brain deficits seen in MetS.
This meta-analysis explores the location and function of brain areas involved in social cognition, or the capacity to understand people's behavioral intentions, social beliefs, and personality traits. On the basis of over 200 fMRI studies, it tests alternative theoretical proposals that attempt to explain how several brain areas process information relevant for social cognition. The results suggest that inferring temporary states such as goals, intentions, and desires of other people-even when they are false and unjust from our own perspective--strongly engages the temporo-parietal junction (TPJ). Inferring more enduring dispositions of others and the self, or interpersonal norms and scripts, engages the medial prefrontal cortex (mPFC), although temporal states can also activate the mPFC. Other candidate tasks reflecting general-purpose brain processes that may potentially subserve social cognition are briefly reviewed, such as sequence learning, causality detection, emotion processing, and executive functioning (action monitoring, attention, dual task monitoring, episodic memory retrieval), but none of them overlaps uniquely with the regions activated during social cognition. Hence, it appears that social cognition particularly engages the TPJ and mPFC regions. The available evidence is consistent with the role of a TPJ-related mirror system for inferring temporary goals and intentions at a relatively perceptual level of representation, and the mPFC as a module that integrates social information across time and allows reflection and representation of traits and norms, and presumably also of intentionality, at a more abstract cognitive level.
Modifiable risk and protective factors for boosting brain and cognitive development and preventing neurodegeneration and cognitive decline are embraced in neuroimaging studies. We call for sobriety regarding the timing and quantity of such influences on brain and cognition. Individual differences in the level of brain and cognition, many of which present already at birth and early in development, appear stable, larger, and more pervasive than differences in change across the lifespan. Incorporating early-life factors, including genetics, and investigating both level and change will reduce the risk of ascribing undue importance and causality to proximate factors in adulthood and older age. This has implications for both mechanistic understanding and prevention.
Until recently, clinicians and researchers have performed gait assessments and cognitive assessments separately when evaluating older adults, but increasing evidence from clinical practice, epidemiological studies, and clinical trials shows that gait and cognition are interrelated in older adults. Quantifiable alterations in gait in older adults are associated with falls, dementia, and disability. At the same time, emerging evidence indicates that early disturbances in cognitive processes such as attention, executive function, and working memory are associated with slower gait and gait instability during single- and dual-task testing and that these cognitive disturbances assist in the prediction of future mobility loss, falls, and progression to dementia. This article reviews the importance of the interrelationship between gait and cognition in aging and presents evidence that gait assessments can provide a window into the understanding of cognitive function and dysfunction and fall risk in older people in clinical practice. To this end, the benefits of dual-task gait assessments (e.g., walking while performing an attention-demanding task) as a marker of fall risk are summarized. A potential complementary approach for reducing the risk of falls by improving certain aspects of cognition through nonpharmacological and pharmacological treatments is also presented. Untangling the relationship between early gait disturbances and early cognitive changes may be helpful in identifying older adults at risk of experiencing mobility decline, falls, and progression to dementia.
This is a brief review of current evidence for the relationships between physical activity and exercise and the brain and cognition throughout the life span in non-pathological populations. We focus on the effects of both aerobic and resistance training and provide a brief overview of potential neurobiological mechanisms derived from non-human animal models. Whereas research has focused primarily on the benefits of aerobic exercise in youth and young adult populations, there is growing evidence that both aerobic and resistance training are important for maintaining cognitive and brain health in old age. Finally, in these contexts, we point out gaps in the literature and future directions that will help advance the field of exercise neuroscience, including more studies that explicitly examine the effect of exercise type and intensity on cognition, the brain, and clinically significant outcomes. There is also a need for human neuroimaging studies to adopt a more unified multi-modal framework and for greater interaction between human and animal models of exercise effects on brain and cognition across the life span.
PURPOSE: Physical activity (PA) is known to improve cognitive and brain function, but debate continues regarding the consistency and magnitude of its effects, populations and cognitive domains most affected, and parameters necessary to achieve the greatest improvements (e.g., dose). METHODS: In this umbrella review conducted in part for the 2018 Health and Human Services Physical Activity Guidelines for Americans Advisory Committee, we examined whether PA interventions enhance cognitive and brain outcomes across the life span, as well as in populations experiencing cognitive dysfunction (e.g., schizophrenia). Systematic reviews, meta-analyses, and pooled analyses were used. We further examined whether engaging in greater amounts of PA is associated with a reduced risk of developing cognitive impairment and dementia in late adulthood. RESULTS: Moderate evidence from randomized controlled trials indicates an association between moderate- to vigorous-intensity PA and improvements in cognition, including performance on academic achievement and neuropsychological tests, such as those measuring processing speed, memory, and executive function. Strong evidence demonstrates that acute bouts of moderate- to vigorous-intensity PA have transient benefits for cognition during the postrecovery period after exercise. Strong evidence demonstrates that greater amounts of PA are associated with a reduced risk of developing cognitive impairment, including Alzheimer's disease. The strength of the findings varies across the life span and in individuals with medical conditions influencing cognition. CONCLUSIONS: There is moderate-to-strong support that PA benefits cognitive functioning during early and late periods of the life span and in certain populations characterized by cognitive deficits.
OBJECTIVE: Decline in cognitive function begins by the 40s, and may be related to future dementia risk. We used data from a community-representative study to determine whether there are age-related differences in simple cognitive and gait tests by the 40s, and whether these differences were associated with covert cerebrovascular disease on magnetic resonance imaging (MRI). METHODS: Between 2010 and 2012, 803 participants aged 40 to 75 years in the Prospective Urban Rural Epidemiological (PURE) study, recruited from prespecified postal code regions centered on 4 Canadian cities, underwent brain MRI and simple tests of cognition and gait as part of a substudy (PURE-MIND). RESULTS: Mean age was 58 ± 8 years. Linear decreases in performance on the Montreal Cognitive Assessment, Digit Symbol Substitution Test (DSST), and Timed Up and Go test of gait were seen with each age decade from the 40s to the 70s. Silent brain infarcts were observed in 3% of 40- to 49-year-olds, with increasing prevalence up to 18.9% in 70-year-olds. Silent brain infarcts were associated with slower timed gait and lower volume of supratentorial white matter. Higher volume of supratentorial MRI white matter hyperintensity was associated with slower timed gait and worse performance on DSST, and lower volumes of the supratentorial cortex and white matter, and cerebellum. INTERPRETATION: Covert cerebrovascular disease and its consequences on cognitive and gait performance and brain atrophy are manifest in some clinically asymptomatic persons as early as the 5th decade of life.
Since the last common ancestor shared by modern humans, chimpanzees and bonobos, the lineage leading to Homo sapiens has undergone a substantial change in brain size and organization. As a result, modern humans display striking differences from the living apes in the realm of cognition and linguistic expression. In this article, we review the evolutionary changes that occurred in the descent of Homo sapiens by reconstructing the neural and cognitive traits that would have characterized the last common ancestor and comparing these with the modern human condition. The last common ancestor can be reconstructed to have had a brain of approximately 300-400 g that displayed several unique phylogenetic specializations of development, anatomical organization, and biochemical function. These neuroanatomical substrates contributed to the enhancement of behavioral flexibility and social cognition. With this evolutionary history as precursor, the modern human mind may be conceived as a mosaic of traits inherited from a common ancestry with our close relatives, along with the addition of evolutionary specializations within particular domains. These modern human-specific cognitive and linguistic adaptations appear to be correlated with enlargement of the neocortex and related structures. Accompanying this general neocortical expansion, certain higher-order unimodal and multimodal cortical areas have grown disproportionately relative to primary cortical areas. Anatomical and molecular changes have also been identified that might relate to the greater metabolic demand and enhanced synaptic plasticity of modern human brain's. Finally, the unique brain growth trajectory of modern humans has made a significant contribution to our species' cognitive and linguistic abilities.
Low-intensity transcranial focused ultrasound (tFUS) is rapidly emerging as a transformative non-invasive brain stimulation (NIBS) modality characterized by high spatial resolution and ability to target deep brain circuits. Unlike electromagnetic techniques such as transcranial magnetic stimulation and transcranial direct current stimulation, which are constrained by centimeter-scale resolution and a depth-focality tradeoff, tFUS leverages mechanical pressure waves to modulate both superficial cortical and deep subcortical structures with millimeter precision. This article discusses recent scientific observations and engineering breakthroughs in the advancement of tFUS for next-generation ultrasonic brain-computer interfaces (uBCIs) and human-machine interfaces. These advancements move beyond open-loop systems and demonstrate closed-loop architectures that incorporate real-time electrophysiological feedback to optimize cognitive variables such as attention, learning, trust, and cooperation in various applications. Other advances in the development of ultrasound sensors for sonomyography to decode muscle activation and functional ultrasound to monitor hemodynamic brain activity are
Cognition is a core part of and a common topic among philosophy of mind, psychology, neuroscience, AI, and cognitive science. Through a mechanistic lens, I propose a framework of defining, modeling, and analyzing cognition mechanisms. Firstly, appropriate terms are introduced and used in explanations related to the framework and within the definition of a mechanism. I implicitly contend that this terminology essentially characterizes a conceptual world required for discussions in this paper. Secondly, a mathematical model of a mechanism based on directed graphs is proposed. Thirdly, the definition of a base necessary for a mechanism to be classified as a cognition mechanism is proposed. I argue that the cognition base has the features of the cognition self of humans. Fourthly, three ways to mechanistically look at mechanisms is defined and specific instances of them are suggested. Fifthly, standards for visualization and presentation of mechanisms, cognition mechanisms, and the instances to mechanistically look at them are suggested and used to analyze cognition mechanisms through appropriate examples. Finally, the features of this paper are discussed and prospects of further devel
Brain decoding is a key neuroscience field that reconstructs the visual stimuli from brain activity with fMRI, which helps illuminate how the brain represents the world. fMRI-to-image reconstruction has achieved impressive progress by leveraging diffusion models. However, brain signals infused with prior knowledge and associations exhibit a significant information asymmetry when compared to raw visual features, still posing challenges for decoding fMRI representations under the supervision of images. Consequently, the reconstructed images often lack fine-grained visual fidelity, such as missing attributes and distorted spatial relationships. To tackle this challenge, we propose BrainCognizer, a novel brain decoding model inspired by human visual cognition, which explores multi-level semantics and correlations without fine-tuning of generative models. Specifically, BrainCognizer introduces two modules: the Cognitive Integration Module which incorporates prior human knowledge to extract hierarchical region semantics; and the Cognitive Correlation Module which captures contextual semantic relationships across regions. Incorporating these two modules enhances intra-region semantic cons
Background: People with bipolar disorder (BD) tend to show widespread cognitive impairment compared to healthy controls. Impairments in processing speed (PS), attention, and executive function (EF) may represent 'core' impairments that have a role in wider cognitive dysfunction. Cognitive impairments appear to relate to structural brain abnormalities in BD, but whether core deficits are related to particular brain regions is unclear and much of the research on brain-cognition associations is limited by univariate analysis and small samples. Methods: Euthymic BD patients (n=56) and matched healthy controls (n=26) underwent T1-weighted MRI scans and completed neuropsychological tests of PS, attention, and EF. We utilised public datasets to develop a normative model of cortical thickness (n=5,977) to generate robust estimations of cortical abnormalities in patients. Canonical correlation analysis was used to assess multivariate brain-cognition associations in BD, controlling for age, sex, and premorbid IQ. Results: BD showed impairments on tests of PS, attention, and EF, and abnormal cortical thickness in several brain regions compared to healthy controls. Impairments in tests of PS a
Background: Brain network models offer insights into brain dynamics, but the utility of model-derived bifurcation parameters as biomarkers remains underexplored. Objective: This study evaluates bifurcation parameters from a whole-brain network model as biomarkers for distinguishing brain states associated with resting-state and task-based cognitive conditions. Methods: Synthetic BOLD signals were generated using a supercritical Hopf brain network model to train deep learning models for bifurcation parameter prediction. Inference was performed on Human Connectome Project data, including both resting-state and task-based conditions. Statistical analyses assessed the separability of brain states based on bifurcation parameter distributions. Results: Bifurcation parameter distributions differed significantly across task and resting-state conditions ($p < 0.0001$ for all but one comparison). Task-based brain states exhibited higher bifurcation values compared to rest. Conclusion: Bifurcation parameters effectively differentiate cognitive and resting states, warranting further investigation as biomarkers for brain state characterization and neurological disorder assessment.
Diverse cognitive processes set different demands on locally segregated and globally integrated brain activity. However, it remains unclear how resting brains configure their functional organization to balance the demands on network segregation and integration to best serve cognition. Here, we use an eigenmode-based approach to identify hierarchical modules in functional brain networks, and quantify the functional balance between network segregation and integration. In a large sample of healthy young adults (n=991), we combine the whole-brain resting state functional magnetic resonance imaging (fMRI) data with a mean-filed model on the structural network derived from diffusion tensor imaging and demonstrate that resting brain networks are on average close to a balanced state. This state allows for a balanced time dwelling at segregated and integrated configurations, and highly flexible switching between them. Furthermore, we employ structural equation modelling to estimate general and domain-specific cognitive phenotypes from nine tasks, and demonstrate that network segregation, integration and their balance in resting brains predict individual differences in diverse cognitive phen