Multimodal fusion analysis of functional, cerebrovascular and structural neuroimaging in healthy aging subjects.

Brain aging is a complex process that requires a multimodal approach. Neuroimaging can provide insights into brain morphology, functional organization, and vascular dynamics. However, most neuroimaging studies of aging have focused on each imaging modality separately, limiting the understanding of interrelations between processes identified by different modalities and their relevance to cognitive decline in aging. Here, we used a data-driven multimodal approach, linked independent component analysis (ICA), to jointly analyze magnetic resonance imaging (MRI) of grey matter volume, cerebrovascular, and functional network topographies in relation to measures of fluid intelligence. Neuroimaging and cognitive data from the Cambridge Centre for Ageing and Neuroscience study were used, with healthy participants aged 18-88 years (main dataset n = 215 and secondary dataset n = 433). Using linked ICA, functional network activities were characterized in independent components but not captured in the same component as structural and cerebrovascular patterns. Split-sample (n = 108/107) and out-of-sample (n = 433) validation analyses using linked ICA were also performed. Global grey matter volume with regional cerebrovascular changes and the right frontoparietal network activity were correlated with age-related and individual differences in fluid intelligence. This study presents the insights from linked ICA to bring together measurements from multiple imaging modalities, with independent and additive information. We propose that integrating multiple neuroimaging modalities allows better characterization of brain pattern variability and changes associated with healthy aging.

Decoding the Real-Time Neurobiological Properties of Incremental Semantic Interpretation.

Communication through spoken language is a central human capacity, involving a wide range of complex computations that incrementally interpret each word into meaningful sentences. However, surprisingly little is known about the spatiotemporal properties of the complex neurobiological systems that support these dynamic predictive and integrative computations. Here, we focus on prediction, a core incremental processing operation guiding the interpretation of each upcoming word with respect to its preceding context. To investigate the neurobiological basis of how semantic constraints change and evolve as each word in a sentence accumulates over time, in a spoken sentence comprehension study, we analyzed the multivariate patterns of neural activity recorded by source-localized electro/magnetoencephalography (EMEG), using computational models capturing semantic constraints derived from the prior context on each upcoming word. Our results provide insights into predictive operations subserved by different regions within a bi-hemispheric system, which over time generate, refine, and evaluate constraints on each word as it is heard.

Neural dynamics of semantic composition.

Human speech comprehension is remarkable for its immediacy and rapidity. The listener interprets an incrementally delivered auditory input, millisecond by millisecond as it is heard, in terms of complex multilevel representations of relevant linguistic and nonlinguistic knowledge. Central to this process are the neural computations involved in semantic combination, whereby the meanings of words are combined into more complex representations, as in the combination of a verb and its following direct object (DO) noun (e.g., "eat the apple"). These combinatorial processes form the backbone for incremental interpretation, enabling listeners to integrate the meaning of each word as it is heard into their dynamic interpretation of the current utterance. Focusing on the verb-DO noun relationship in simple spoken sentences, we applied multivariate pattern analysis and computational semantic modeling to source-localized electro/magnetoencephalographic data to map out the specific representational constraints that are constructed as each word is heard, and to determine how these constraints guide the interpretation of subsequent words in the utterance. Comparing context-independent semantic models of the DO noun with contextually constrained noun models reflecting the semantic properties of the preceding verb, we found that only the contextually constrained model showed a significant fit to the brain data. Pattern-based measures of directed connectivity across the left hemisphere language network revealed a continuous information flow among temporal, inferior frontal, and inferior parietal regions, underpinning the verb's modification of the DO noun's activated semantics. These results provide a plausible neural substrate for seamless real-time incremental interpretation on the observed millisecond time scales.

Balancing Prediction and Sensory Input in Speech Comprehension: The Spatiotemporal Dynamics of Word Recognition in Context.

Spoken word recognition in context is remarkably fast and accurate, with recognition times of ∼200 ms, typically well before the end of the word. The neurocomputational mechanisms underlying these contextual effects are still poorly understood. This study combines source-localized electroencephalographic and magnetoencephalographic (EMEG) measures of real-time brain activity with multivariate representational similarity analysis to determine directly the timing and computational content of the processes evoked as spoken words are heard in context, and to evaluate the respective roles of bottom-up and predictive processing mechanisms in the integration of sensory and contextual constraints. Male and female human participants heard simple (modifier-noun) English phrases that varied in the degree of semantic constraint that the modifier (W1) exerted on the noun (W2), as in pairs, such as "yellow banana." We used gating tasks to generate estimates of the probabilistic predictions generated by these constraints as well as measures of their interaction with the bottom-up perceptual input for W2. Representation similarity analysis models of these measures were tested against electroencephalographic and magnetoencephalographic brain data across a bilateral fronto-temporo-parietal language network. Consistent with probabilistic predictive processing accounts, we found early activation of semantic constraints in frontal cortex (LBA45) as W1 was heard. The effects of these constraints (at 100 ms after W2 onset in left middle temporal gyrus and at 140 ms in left Heschl's gyrus) were only detectable, however, after the initial phonemes of W2 had been heard. Within an overall predictive processing framework, bottom-up sensory inputs are still required to achieve early and robust spoken word recognition in context.SIGNIFICANCE STATEMENT Human listeners recognize spoken words in natural speech contexts with remarkable speed and accuracy, often identifying a word well before all of it has been heard. In this study, we investigate the brain systems that support this important capacity, using neuroimaging techniques that can track real-time brain activity during speech comprehension. This makes it possible to locate the brain areas that generate predictions about upcoming words and to show how these expectations are integrated with the evidence provided by the speech being heard. We use the timing and localization of these effects to provide the most specific account to date of how the brain achieves an optimal balance between prediction and sensory input in the interpretation of spoken language.

Activity and Connectivity Differences Underlying Inhibitory Control Across the Adult Life Span.

Inhibitory control requires precise regulation of activity and connectivity within multiple brain networks. Previous studies have typically evaluated age-related changes in regional activity or changes in interregional interactions. Instead, we test the hypothesis that activity and connectivity make distinct, complementary contributions to performance across the life span and the maintenance of successful inhibitory control systems. A representative sample of healthy human adults in a large, population-based life span cohort performed an integrated Stop-Signal (SS)/No-Go task during functional magnetic resonance imaging (n = 119; age range, 18-88 years). Individual differences in inhibitory control were measured in terms of the SS reaction time (SSRT), using the blocked integration method. Linear models and independent components analysis revealed that individual differences in SSRT correlated with both activity and connectivity in a distributed inhibition network, comprising prefrontal, premotor, and motor regions. Importantly, this pattern was moderated by age, such that the association between inhibitory control and connectivity, but not activity, differed with age. Multivariate statistics and out-of-sample validation tests of multifactorial functional organization identified differential roles of activity and connectivity in determining an individual's SSRT across the life span. We propose that age-related differences in adaptive cognitive control are best characterized by the joint consideration of multifocal activity and connectivity within distributed brain networks. These insights may facilitate the development of new strategies to support cognitive ability in old age.SIGNIFICANCE STATEMENT The preservation of cognitive and motor control is crucial for maintaining well being across the life span. We show that such control is determined by both activity and connectivity within distributed brain networks. In a large, population-based cohort, we used a novel whole-brain multivariate approach to estimate the functional components of inhibitory control, in terms of their activity and connectivity. Both activity and connectivity in the inhibition network changed with age. But only the association between performance and connectivity, not activity, differed with age. The results suggest that adaptive control is best characterized by the joint consideration of multifocal activity and connectivity. These insights may facilitate the development of new strategies to maintain cognitive ability across the life span in health and disease.

Decoding the Cortical Dynamics of Sound-Meaning Mapping.

Comprehending speech involves the rapid and optimally efficient mapping from sound to meaning. Influential cognitive models of spoken word recognition (Marslen-Wilson and Welsh, 1978) propose that the onset of a spoken word initiates a continuous process of activation of the lexical and semantic properties of the word candidates matching the speech input and competition between them, which continues until the point at which the word is differentiated from all other cohort candidates (the uniqueness point, UP). At this point, the word is recognized uniquely and only the target word's semantics are active. Although it is well established that spoken word recognition engages the superior (Rauschecker and Scott, 2009), middle, and inferior (Hickok and Poeppel, 2007) temporal cortices, little is known about the real-time brain activity that underpins the computations and representations that evolve over time during the transformation from speech to meaning. Here, we test for the first time the spatiotemporal dynamics of these processes by collecting MEG data while human participants listened to spoken words. By constructing quantitative models of competition and access to meaning in combination with spatiotemporal searchlight representational similarity analysis (Kriegeskorte et al., 2006) in source space, we were able to test where and when these models produced significant effects. We found early transient effects ∼400 ms before the UP of lexical competition in left supramarginal gyrus, left superior temporal gyrus, left middle temporal gyrus (MTG), and left inferior frontal gyrus (IFG) and of semantic competition in MTG, left angular gyrus, and IFG. After the UP, there were no competitive effects, only target-specific semantic effects in angular gyrus and MTG. SIGNIFICANCE STATEMENT: Understanding spoken words involves complex processes that transform the auditory input into a meaningful interpretation. This effortless transition occurs on millisecond timescales, with remarkable speed and accuracy and without any awareness of the complex computations involved. Here, we reveal the real-time neural dynamics of these processes by collecting data about listeners' brain activity as they hear spoken words. Using novel statistical models of different aspects of the recognition process, we can locate directly which parts of the brain are accessing the stored form and meaning of words and how the competition between different word candidates is resolved neurally in real time. This gives us a uniquely differentiated picture of the neural substrate for the first 500 ms of word recognition.

Oscillatory Dynamics of Perceptual to Conceptual Transformations in the Ventral Visual Pathway.

Object recognition requires dynamic transformations of low-level visual inputs to complex semantic representations. Although this process depends on the ventral visual pathway, we lack an incremental account from low-level inputs to semantic representations and the mechanistic details of these dynamics. Here we combine computational models of vision with semantics and test the output of the incremental model against patterns of neural oscillations recorded with magnetoencephalography in humans. Representational similarity analysis showed visual information was represented in low-frequency activity throughout the ventral visual pathway, and semantic information was represented in theta activity. Furthermore, directed connectivity showed visual information travels through feedforward connections, whereas visual information is transformed into semantic representations through feedforward and feedback activity, centered on the anterior temporal lobe. Our research highlights that the complex transformations between visual and semantic information is driven by feedforward and recurrent dynamics resulting in object-specific semantics.

Extrinsic and Intrinsic Brain Network Connectivity Maintains Cognition across the Lifespan Despite Accelerated Decay of Regional Brain Activation.

The maintenance of wellbeing across the lifespan depends on the preservation of cognitive function. We propose that successful cognitive aging is determined by interactions both within and between large-scale functional brain networks. Such connectivity can be estimated from task-free functional magnetic resonance imaging (fMRI), also known as resting-state fMRI (rs-fMRI). However, common correlational methods are confounded by age-related changes in the neurovascular signaling. To estimate network interactions at the neuronal rather than vascular level, we used generative models that specified both the neural interactions and a flexible neurovascular forward model. The networks' parameters were optimized to explain the spectral dynamics of rs-fMRI data in 602 healthy human adults from population-based cohorts who were approximately uniformly distributed between 18 and 88 years (www.cam-can.com). We assessed directed connectivity within and between three key large-scale networks: the salience network, dorsal attention network, and default mode network. We found that age influences connectivity both within and between these networks, over and above the effects on neurovascular coupling. Canonical correlation analysis revealed that the relationship between network connectivity and cognitive function was age-dependent: cognitive performance relied on neural dynamics more strongly in older adults. These effects were driven partly by reduced stability of neural activity within all networks, as expressed by an accelerated decay of neural information. Our findings suggest that the balance of excitatory connectivity between networks, and the stability of intrinsic neural representations within networks, changes with age. The cognitive function of older adults becomes increasingly dependent on these factors. SIGNIFICANCE STATEMENT: Maintaining cognitive function is critical to successful aging. To study the neural basis of cognitive function across the lifespan, we studied a large population-based cohort (n = 602, 18-88 years), separating neural connectivity from vascular components of fMRI signals. Cognitive ability was influenced by the strength of connection within and between functional brain networks, and this positive relationship increased with age. In older adults, there was more rapid decay of intrinsic neuronal activity in multiple regions of the brain networks, which related to cognitive performance. Our data demonstrate increased reliance on network flexibility to maintain cognitive function, in the presence of more rapid decay of neural activity. These insights will facilitate the development of new strategies to maintain cognitive ability.

Robust Resilience of the Frontotemporal Syntax System to Aging.

UNLABELLED: Brain function is thought to become less specialized with age. However, this view is largely based on findings of increased activation during tasks that fail to separate task-related processes (e.g., attention, decision making) from the cognitive process under examination. Here we take a systems-level approach to separate processes specific to language comprehension from those related to general task demands and to examine age differences in functional connectivity both within and between those systems. A large population-based sample (N = 111; 22-87 years) from the Cambridge Centre for Aging and Neuroscience (Cam-CAN) was scanned using functional MRI during two versions of an experiment: a natural listening version in which participants simply listened to spoken sentences and an explicit task version in which they rated the acceptability of the same sentences. Independent components analysis across the combined data from both versions showed that although task-free language comprehension activates only the auditory and frontotemporal (FTN) syntax networks, performing a simple task with the same sentences recruits several additional networks. Remarkably, functionality of the critical FTN is maintained across age groups, showing no difference in within-network connectivity or responsivity to syntactic processing demands despite gray matter loss and reduced connectivity to task-related networks. We found no evidence for reduced specialization or compensation with age. Overt task performance was maintained across the lifespan and performance in older, but not younger, adults related to crystallized knowledge, suggesting that decreased between-network connectivity may be compensated for by older adults' richer knowledge base. SIGNIFICANCE STATEMENT: Understanding spoken language requires the rapid integration of information at many different levels of analysis. Given the complexity and speed of this process, it is remarkably well preserved with age. Although previous work claims that this preserved functionality is due to compensatory activation of regions outside the frontotemporal language network, we use a novel systems-level approach to show that these "compensatory" activations simply reflect age differences in response to experimental task demands. Natural, task-free language comprehension solely recruits auditory and frontotemporal networks, the latter of which is similarly responsive to language-processing demands across the lifespan. These findings challenge the conventional approach to neurocognitive aging by showing that the neural underpinnings of a given cognitive function depend on how you test it.

The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: Structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample.

This paper describes the data repository for the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) initial study cohort. The Cam-CAN Stage 2 repository contains multi-modal (MRI, MEG, and cognitive-behavioural) data from a large (approximately N=700), cross-sectional adult lifespan (18-87years old) population-based sample. The study is designed to characterise age-related changes in cognition and brain structure and function, and to uncover the neurocognitive mechanisms that support healthy cognitive ageing. The database contains raw and preprocessed structural MRI, functional MRI (active tasks and resting state), and MEG data (active tasks and resting state), as well as derived scores from cognitive behavioural experiments spanning five broad domains (attention, emotion, action, language, and memory), and demographic and neuropsychological data. The dataset thus provides a depth of neurocognitive phenotyping that is currently unparalleled, enabling integrative analyses of age-related changes in brain structure, brain function, and cognition, and providing a testbed for novel analyses of multi-modal neuroimaging data.

Learning Warps Object Representations in the Ventral Temporal Cortex.

The human ventral temporal cortex (VTC) plays a critical role in object recognition. Although it is well established that visual experience shapes VTC object representations, the impact of semantic and contextual learning is unclear. In this study, we tracked changes in representations of novel visual objects that emerged after learning meaningful information about each object. Over multiple training sessions, participants learned to associate semantic features (e.g., "made of wood," "floats") and spatial contextual associations (e.g., "found in gardens") with novel objects. fMRI was used to examine VTC activity for objects before and after learning. Multivariate pattern similarity analyses revealed that, after learning, VTC activity patterns carried information about the learned contextual associations of the objects, such that objects with contextual associations exhibited higher pattern similarity after learning. Furthermore, these learning-induced increases in pattern information about contextual associations were correlated with reductions in pattern information about the object's visual features. In a second experiment, we validated that these contextual effects translated to real-life objects. Our findings demonstrate that visual object representations in VTC are shaped by the knowledge we have about objects and show that object representations can flexibly adapt as a consequence of learning with the changes related to the specific kind of newly acquired information.

Predicting the Time Course of Individual Objects with MEG.

To respond appropriately to objects, we must process visual inputs rapidly and assign them meaning. This involves highly dynamic, interactive neural processes through which information accumulates and cognitive operations are resolved across multiple time scales. However, there is currently no model of object recognition which provides an integrated account of how visual and semantic information emerge over time; therefore, it remains unknown how and when semantic representations are evoked from visual inputs. Here, we test whether a model of individual objects--based on combining the HMax computational model of vision with semantic-feature information--can account for and predict time-varying neural activity recorded with magnetoencephalography. We show that combining HMax and semantic properties provides a better account of neural object representations compared with the HMax alone, both through model fit and classification performance. Our results show that modeling and classifying individual objects is significantly improved by adding semantic-feature information beyond ∼200 ms. These results provide important insights into the functional properties of visual processing across time.

Object-specific semantic coding in human perirhinal cortex.

Category-specificity has been demonstrated in the human posterior ventral temporal cortex for a variety of object categories. Although object representations within the ventral visual pathway must be sufficiently rich and complex to support the recognition of individual objects, little is known about how specific objects are represented. Here, we used representational similarity analysis to determine what different kinds of object information are reflected in fMRI activation patterns and uncover the relationship between categorical and object-specific semantic representations. Our results show a gradient of informational specificity along the ventral stream from representations of image-based visual properties in early visual cortex, to categorical representations in the posterior ventral stream. A key finding showed that object-specific semantic information is uniquely represented in the perirhinal cortex, which was also increasingly engaged for objects that are more semantically confusable. These findings suggest a key role for the perirhinal cortex in representing and processing object-specific semantic information that is more critical for highly confusable objects. Our findings extend current distributed models by showing coarse dissociations between objects in posterior ventral cortex, and fine-grained distinctions between objects supported by the anterior medial temporal lobes, including the perirhinal cortex, which serve to integrate complex object information.

The effect of ageing on fMRI: Correction for the confounding effects of vascular reactivity evaluated by joint fMRI and MEG in 335 adults.

In functional magnetic resonance imaging (fMRI) research one is typically interested in neural activity. However, the blood-oxygenation level-dependent (BOLD) signal is a composite of both neural and vascular activity. As factors such as age or medication may alter vascular function, it is essential to account for changes in neurovascular coupling when investigating neurocognitive functioning with fMRI. The resting-state fluctuation amplitude (RSFA) in the fMRI signal (rsfMRI) has been proposed as an index of vascular reactivity. The RSFA compares favourably with other techniques such as breath-hold and hypercapnia, but the latter are more difficult to perform in some populations, such as older adults. The RSFA is therefore a candidate for use in adjusting for age-related changes in vascular reactivity in fMRI studies. The use of RSFA is predicated on its sensitivity to vascular rather than neural factors; however, the extent to which each of these factors contributes to RSFA remains to be characterized. The present work addressed these issues by comparing RSFA (i.e., rsfMRI variability) to proxy measures of (i) cardiovascular function in terms of heart rate (HR) and heart rate variability (HRV) and (ii) neural activity in terms of resting state magnetoencephalography (rsMEG). We derived summary scores of RSFA, a sensorimotor task BOLD activation, cardiovascular function and rsMEG variability for 335 healthy older adults in the population-based Cambridge Centre for Ageing and Neuroscience cohort (Cam-CAN; www.cam-can.com). Mediation analysis revealed that the effects of ageing on RSFA were significantly mediated by vascular factors, but importantly not by the variability in neuronal activity. Furthermore, the converse effects of ageing on the rsMEG variability were not mediated by vascular factors. We then examined the effect of RSFA scaling of task-based BOLD in the sensorimotor task. The scaling analysis revealed that much of the effects of age on task-based activation studies with fMRI do not survive correction for changes in vascular reactivity, and are likely to have been overestimated in previous fMRI studies of ageing. The results from the mediation analysis demonstrate that RSFA is modulated by measures of vascular function and is not driven solely by changes in the variance of neural activity. Based on these findings we propose that the RSFA scaling method is articularly useful in large scale and longitudinal neuroimaging studies of ageing, or with frail participants, where alternative measures of vascular reactivity are impractical.

Optimally efficient neural systems for processing spoken language.

Cognitive models claim that spoken words are recognized by an optimally efficient sequential analysis process. Evidence for this is the finding that nonwords are recognized as soon as they deviate from all real words (Marslen-Wilson 1984), reflecting continuous evaluation of speech inputs against lexical representations. Here, we investigate the brain mechanisms supporting this core aspect of word recognition and examine the processes of competition and selection among multiple word candidates. Based on new behavioral support for optimal efficiency in lexical access from speech, a functional magnetic resonance imaging study showed that words with later nonword points generated increased activation in the left superior and middle temporal gyrus (Brodmann area [BA] 21/22), implicating these regions in dynamic sound-meaning mapping. We investigated competition and selection by manipulating the number of initially activated word candidates (competition) and their later drop-out rate (selection). Increased lexical competition enhanced activity in bilateral ventral inferior frontal gyrus (BA 47/45), while increased lexical selection demands activated bilateral dorsal inferior frontal gyrus (BA 44/45). These findings indicate functional differentiation of the fronto-temporal systems for processing spoken language, with left middle temporal gyrus (MTG) and superior temporal gyrus (STG) involved in mapping sounds to meaning, bilateral ventral inferior frontal gyrus (IFG) engaged in less constrained early competition processing, and bilateral dorsal IFG engaged in later, more fine-grained selection processes.

Representational similarity analysis reveals commonalities and differences in the semantic processing of words and objects.

Understanding the meanings of words and objects requires the activation of underlying conceptual representations. Semantic representations are often assumed to be coded such that meaning is evoked regardless of the input modality. However, the extent to which meaning is coded in modality-independent or amodal systems remains controversial. We address this issue in a human fMRI study investigating the neural processing of concepts, presented separately as written words and pictures. Activation maps for each individual word and picture were used as input for searchlight-based multivoxel pattern analyses. Representational similarity analysis was used to identify regions correlating with low-level visual models of the words and objects and the semantic category structure common to both. Common semantic category effects for both modalities were found in a left-lateralized network, including left posterior middle temporal gyrus (LpMTG), left angular gyrus, and left intraparietal sulcus (LIPS), in addition to object- and word-specific semantic processing in ventral temporal cortex and more anterior MTG, respectively. To explore differences in representational content across regions and modalities, we developed novel data-driven analyses, based on k-means clustering of searchlight dissimilarity matrices and seeded correlation analysis. These revealed subtle differences in the representations in semantic-sensitive regions, with representations in LIPS being relatively invariant to stimulus modality and representations in LpMTG being uncorrelated across modality. These results suggest that, although both LpMTG and LIPS are involved in semantic processing, only the functional role of LIPS is the same regardless of the visual input, whereas the functional role of LpMTG differs for words and objects.

The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study protocol: a cross-sectional, lifespan, multidisciplinary examination of healthy cognitive ageing.

BACKGROUND: As greater numbers of us are living longer, it is increasingly important to understand how we can age healthily. Although old age is often stereotyped as a time of declining mental abilities and inflexibility, cognitive neuroscience reveals that older adults use neural and cognitive resources flexibly, recruiting novel neural regions and cognitive processes when necessary. Our aim in this project is to understand how age-related changes to neural structure and function interact to support cognitive abilities across the lifespan. METHODS/DESIGN: We are recruiting a population-based cohort of 3000 adults aged 18 and over into Stage 1 of the project, where they complete an interview including health and lifestyle questions, a core cognitive assessment, and a self-completed questionnaire of lifetime experiences and physical activity. Of those interviewed, 700 participants aged 18-87 (100 per age decile) continue to Stage 2 where they undergo cognitive testing and provide measures of brain structure and function. Cognition is assessed across multiple domains including attention and executive control, language, memory, emotion, action control and learning. A subset of 280 adults return for in-depth neurocognitive assessment in Stage 3, using functional neuroimaging experiments across our key cognitive domains.Formal statistical models will be used to examine the changes that occur with healthy ageing, and to evaluate age-related reorganisation in terms of cognitive and neural functions invoked to compensate for overall age-related brain structural decline. Taken together the three stages provide deep phenotyping that will allow us to measure neural activity and flexibility during performance across a number of core cognitive functions. This approach offers hypothesis-driven insights into the relationship between brain and behaviour in healthy ageing that are relevant to the general population. DISCUSSION: Our study is a unique resource of neuroimaging and cognitive measures relevant to change across the adult lifespan. Because we focus on normal age-related changes, our results may contribute to changing views about the ageing process, lead to targeted interventions, and reveal how normal ageing relates to frail ageing in clinicopathological conditions such as Alzheimer's disease.

From perception to conception: how meaningful objects are processed over time.

To recognize visual objects, our sensory perceptions are transformed through dynamic neural interactions into meaningful representations of the world but exactly how visual inputs invoke object meaning remains unclear. To address this issue, we apply a regression approach to magnetoencephalography data, modeling perceptual and conceptual variables. Key conceptual measures were derived from semantic feature-based models claiming shared features (e.g., has eyes) provide broad category information, while distinctive features (e.g., has a hump) are additionally required for more specific object identification. Our results show initial perceptual effects in visual cortex that are rapidly followed by semantic feature effects throughout ventral temporal cortex within the first 120 ms. Moreover, these early semantic effects reflect shared semantic feature information supporting coarse category-type distinctions. Post-200 ms, we observed the effects along the extent of ventral temporal cortex for both shared and distinctive features, which together allow for conceptual differentiation and object identification. By relating spatiotemporal neural activity to statistical feature-based measures of semantic knowledge, we demonstrate that qualitatively different kinds of perceptual and semantic information are extracted from visual objects over time, with rapid activation of shared object features followed by concomitant activation of distinctive features that together enable meaningful visual object recognition.

Functional organization of the neural language system: dorsal and ventral pathways are critical for syntax.

The core of human language, which differentiates it from the communicative abilities of other species, is the set of combinatorial operations called syntax. For over a century researchers have attempted to understand how this essential function is organized in the brain. Here, we combine behavioral and neuroimaging methods, with left hemisphere-damaged patients and healthy controls, to identify the pathways connecting the brain regions involved in syntactic processing. In a previous functional magnetic resonance imaging study (Tyler LK, Wright P, Randall B, Marslen-Wilson WD, Stamatakis EA. 2010b. Reorganization of syntactic processing following left-hemisphere brain damage: does right-hemisphere activity preserve function? Brain. 133(11):3396-3408.), we established that regions of left inferior frontal cortex and left posterior middle temporal cortex were activated during syntactic processing. These clusters were used here as seeds for probabilistic tractography analyses in patients and controls, allowing us to delineate, and measure the integrity of, the white matter tracts connecting the frontal and temporal regions active for syntax. We found that structural disconnection in either of 2 fiber bundles--the arcuate fasciculus or the extreme capsule fiber system--was associated with syntactic impairment in patients. The results demonstrate the causal role in syntactic analysis of these 2 major left hemisphere fiber bundles--challenging existing views about their role in language functions and providing a new basis for future research in this key area of human cognition.

The Centre for Speech, Language and the Brain (CSLB) concept property norms.

Theories of the representation and processing of concepts have been greatly enhanced by models based on information available in semantic property norms. This information relates both to the identity of the features produced in the norms and to their statistical properties. In this article, we introduce a new and large set of property norms that are designed to be a more flexible tool to meet the demands of many different disciplines interested in conceptual knowledge representation, from cognitive psychology to computational linguistics. As well as providing all features listed by 2 or more participants, we also show the considerable linguistic variation that underlies each normalized feature label and the number of participants who generated each variant. Our norms are highly comparable with the largest extant set (McRae, Cree, Seidenberg, & McNorgan, 2005) in terms of the number and distribution of features. In addition, we show how the norms give rise to a coherent category structure. We provide these norms in the hope that the greater detail available in the Centre for Speech, Language and the Brain norms should further promote the development of models of conceptual knowledge. The norms can be downloaded at www.csl.psychol.cam.ac.uk/propertynorms.