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Philosophical Transactions of the Royal... Oct 2016Studies are described which are intended to improve our understanding of the primary measurements made in non-invasive neural imaging. The blood oxygenation... (Review)
Review
Studies are described which are intended to improve our understanding of the primary measurements made in non-invasive neural imaging. The blood oxygenation level-dependent signal used in functional magnetic resonance imaging (fMRI) reflects changes in deoxygenated haemoglobin. Tissue oxygen concentration, along with blood flow, changes during neural activation. Therefore, measurements of tissue oxygen together with the use of a neural sensor can provide direct estimates of neural-metabolic interactions. We have used this relationship in a series of studies in which a neural microelectrode is combined with an oxygen micro-sensor to make simultaneous co-localized measurements in the central visual pathway. Oxygen responses are typically biphasic with small initial dips followed by large secondary peaks during neural activation. By the use of established visual response characteristics, we have determined that the oxygen initial dip provides a better estimate of local neural function than the positive peak. This contrasts sharply with fMRI for which the initial dip is unreliable. To extend these studies, we have examined the relationship between the primary metabolic agents, glucose and lactate, and associated neural activity. For this work, we also use a Doppler technique to measure cerebral blood flow (CBF) together with neural activity. Results show consistent synchronously timed changes such that increases in neural activity are accompanied by decreases in glucose and simultaneous increases in lactate. Measurements of CBF show clear delays with respect to neural response. This is consistent with a slight delay in blood flow with respect to oxygen delivery during neural activation.This article is part of the themed issue 'Interpreting BOLD: a dialogue between cognitive and cellular neuroscience'.
Topics: Animals; Brain Mapping; Cats; Cerebrovascular Circulation; Glucose; Lactic Acid; Magnetic Resonance Imaging; Neurons; Oxygen; Visual Pathways
PubMed: 27574310
DOI: 10.1098/rstb.2015.0357 -
Indian Journal of Ophthalmology Jun 1996Amblyopia is an acquired defect in vision due to an abnormal visual experience during a sensitive period of visual development. The neuronal basis of amblyopia is the... (Review)
Review
Amblyopia is an acquired defect in vision due to an abnormal visual experience during a sensitive period of visual development. The neuronal basis of amblyopia is the study of the effects of "abnormal" environmental influences on the genetically programmed development of the visual processing system. Visual pathway development commences with ganglion cells forming the optic nerve. The process that guides these neurones initially to the lateral geniculate nucleus (LGN) and then onto the visual cortex is genetically programmed. Initially this process is influenced by spontaneously generated impulses and neurotrophic factors. Following birth, visual stimuli modify and refine the genetically programmed process. Exposure to the visual environment includes the risk of abnormal inputs. Abnormal stimuli disrupt the formation of patterned inputs allowing alteration of visual cortical writing with reduction in ocular dominance columns driven by the abnormal eye. Correction of the abnormal visual input and penalisation of the "normal" input is the mainstay of therapy for amblyopia. Further understanding of the mechanisms involved in the development of a normal visual processing system will allow trialing therapies for amblyopia not responding to occlusion therapy. Levodopa is one agent providing insights into recovery of visual function for short periods in apparently mature visual systems.
Topics: Amblyopia; Animals; Geniculate Bodies; Humans; Neurons; Visual Cortex; Visual Pathways
PubMed: 8916592
DOI: No ID Found -
Neural Plasticity 2018Alzheimer's disease (AD) is the leading cause of dementia worldwide. It compromises patients' daily activities owing to progressive cognitive deterioration, which has... (Review)
Review
Alzheimer's disease (AD) is the leading cause of dementia worldwide. It compromises patients' daily activities owing to progressive cognitive deterioration, which has elevated direct and indirect costs. Although AD has several risk factors, aging is considered the most important. Unfortunately, clinical diagnosis is usually performed at an advanced disease stage when dementia is established, making implementation of successful therapeutic interventions difficult. Current biomarkers tend to be expensive, insufficient, or invasive, raising the need for novel, improved tools aimed at early disease detection. AD is characterized by brain atrophy due to neuronal and synaptic loss, extracellular amyloid plaques composed of amyloid-beta peptide (A), and neurofibrillary tangles of hyperphosphorylated tau protein. The visual system and central nervous system share many functional components. Thus, it is plausible that damage induced by A, tau, and neuroinflammation may be observed in visual components such as the retina, even at an early disease stage. This underscores the importance of implementing ophthalmological examinations, less invasive and expensive than other biomarkers, as useful measures to assess disease progression and severity in individuals with or at risk of AD. Here, we review functional and morphological changes of the retina and visual pathway in AD from pathophysiological and clinical perspectives.
Topics: Alzheimer Disease; Amyloid beta-Protein Precursor; Disease Progression; Humans; Plaque, Amyloid; Retina; Vision Disorders; Visual Pathways; tau Proteins
PubMed: 30405709
DOI: 10.1155/2018/2941783 -
Acta Ophthalmologica Mar 2016Many eye diseases reduce visual acuity or are associated with visual field defects. Because of the well-defined retinotopic organization of the connections of the visual... (Review)
Review
Many eye diseases reduce visual acuity or are associated with visual field defects. Because of the well-defined retinotopic organization of the connections of the visual pathways, this may affect specific parts of the visual pathways and cortex, as a result of either deprivation or transsynaptic degeneration. For this reason, over the past several years, numerous structural magnetic resonance imaging (MRI) studies have examined the association of eye diseases with pathway and brain changes. Here, we review structural MRI studies performed in human patients with the eye diseases albinism, amblyopia, hereditary retinal dystrophies, age-related macular degeneration (AMD) and glaucoma. We focus on two main questions. First, what have these studies revealed? Second, what is the potential clinical relevance of their findings? We find that all the aforementioned eye diseases are indeed associated with structural changes in the visual pathways and brain. As such changes have been described in very different eye diseases, in our view the most parsimonious explanation is that these are caused by the loss of visual input and the subsequent deprivation of the visual pathways and brain regions, rather than by transsynaptic degeneration. Moreover, and of clinical relevance, for some of the diseases - in particular glaucoma and AMD - present results are compatible with the view that the eye disease is part of a more general neurological or neurodegenerative disorder that also affects the brain. Finally, establishing structural changes of the visual pathways has been relevant in the context of new therapeutic strategies to restore retinal function: it implies that restoring retinal function may not suffice to also effectively restore vision. Future structural MRI studies can contribute to (i) further establish relationships between ocular and neurological neurodegenerative disorders, (ii) investigate whether brain degeneration in eye diseases is reversible, (iii) evaluate the use of neuroprotective medication in ocular disease, (iv) determine optimal timing for retinal implant insertion and (v) establish structural MRI examination as a diagnostic tool in ophthalmology.
Topics: Brain Diseases; Eye Diseases; Humans; Magnetic Resonance Imaging; Vision Disorders; Visual Acuity; Visual Cortex; Visual Fields; Visual Pathways
PubMed: 26361248
DOI: 10.1111/aos.12825 -
Neuroscience Bulletin Sep 2020
Topics: Animals; Humans; Vision, Binocular; Visual Cortex; Visual Pathways
PubMed: 32367252
DOI: 10.1007/s12264-020-00506-6 -
Neuron Apr 2012Visual area V4 is a midtier cortical area in the ventral visual pathway. It is crucial for visual object recognition and has been a focus of many studies on visual... (Review)
Review
Visual area V4 is a midtier cortical area in the ventral visual pathway. It is crucial for visual object recognition and has been a focus of many studies on visual attention. However, there is no unifying view of V4's role in visual processing. Neither is there an understanding of how its role in feature processing interfaces with its role in visual attention. This review captures our current knowledge of V4, largely derived from electrophysiological and imaging studies in the macaque monkey. Based on recent discovery of functionally specific domains in V4, we propose that the unifying function of V4 circuitry is to enable selective extraction of specific functional domain-based networks, whether it be by bottom-up specification of object features or by top-down attentionally driven selection.
Topics: Animals; Attention; Macaca; Mental Processes; Visual Cortex; Visual Pathways; Visual Perception
PubMed: 22500626
DOI: 10.1016/j.neuron.2012.03.011 -
Neurology(R) Neuroimmunology &... Mar 2020
Topics: Afferent Pathways; Gray Matter; Humans; Optic Neuritis; Visual Pathways
PubMed: 32229640
DOI: 10.1212/NXI.0000000000000667 -
Frontiers in Neural Circuits 2019Based on stimulation with plaid patterns, neurons in the Middle Temporal (MT) area of primate visual cortex are divided into two types: pattern and component cells. The...
Based on stimulation with plaid patterns, neurons in the Middle Temporal (MT) area of primate visual cortex are divided into two types: pattern and component cells. The prevailing theory suggests that pattern selectivity results from the summation of the outputs of component cells as part of a hierarchical visual pathway. We present a computational model of the visual pathway from primary visual cortex (V1) to MT that suggests an alternate model where the progression from component to pattern selectivity is not required. Using standard orientation-selective V1 cells, end-stopped V1 cells, and V1 cells with extra-classical receptive fields (RFs) as inputs to MT, the model shows that the degree of pattern or component selectivity in MT could arise from the relative strengths of the three V1 input types. Dominance of end-stopped V1 neurons in the model leads to pattern selectivity in MT, while dominance of V1 cells with extra-classical RFs result in component selectivity. This model may assist in designing experiments to further understand motion processing mechanisms in primate MT.
Topics: Animals; Computer Simulation; Humans; Models, Neurological; Motion Perception; Neurons; Pattern Recognition, Visual; Visual Cortex; Visual Pathways
PubMed: 31293393
DOI: 10.3389/fncir.2019.00043 -
Journal of Neurophysiology Dec 2019Searching for a specific visual object requires our brain to compare the items in view with a remembered representation of the sought target to determine whether a...
Searching for a specific visual object requires our brain to compare the items in view with a remembered representation of the sought target to determine whether a target match is present. This comparison is thought to be implemented, in part, via the combination of top-down modulations reflecting target identity with feed-forward visual representations. However, it remains unclear whether top-down signals are integrated at a single locus within the ventral visual pathway (e.g., V4) or at multiple stages [e.g., both V4 and inferotemporal cortex (IT)]. To investigate, we recorded neural responses in V4 and IT as rhesus monkeys performed a task that required them to identify when a target object appeared across variation in position, size, and background context. We found nonvisual, task-specific signals in both V4 and IT. To evaluate whether V4 was the only locus for the integration of top-down signals, we evaluated several feed-forward accounts of processing from V4 to IT, including a model in which IT preferentially sampled from the best V4 units and a model that allowed for nonlinear IT computation. IT task-specific modulation was not accounted for by any of these feed-forward descriptions, suggesting that during object search, top-down signals are integrated directly within IT. To find specific objects, the brain must integrate top-down, target-specific signals with visual information about objects in view. However, the exact route of this integration in the ventral visual pathway is unclear. In the first study to systematically compare V4 and inferotemporal cortex (IT) during an invariant object search task, we demonstrate that top-down signals found in IT cannot be described as being inherited from V4 but rather must be integrated directly within IT itself.
Topics: Animals; Attention; Behavior, Animal; Electrocorticography; Macaca mulatta; Male; Pattern Recognition, Visual; Temporal Lobe; Visual Cortex; Visual Pathways
PubMed: 31618085
DOI: 10.1152/jn.00024.2019 -
The Journal of Neuroscience : the... Aug 2019Recent studies showed agreement between how the human brain and neural networks represent objects, suggesting that we might start to understand the underlying...
Recent studies showed agreement between how the human brain and neural networks represent objects, suggesting that we might start to understand the underlying computations. However, we know that the human brain is prone to biases at many perceptual and cognitive levels, often shaped by learning history and evolutionary constraints. Here, we explore one such perceptual phenomenon, perceiving animacy, and use the performance of neural networks as a benchmark. We performed an fMRI study that dissociated object appearance (what an object looks like) from object category (animate or inanimate) by constructing a stimulus set that includes animate objects (e.g., a cow), typical inanimate objects (e.g., a mug), and, crucially, inanimate objects that look like the animate objects (e.g., a cow mug). Behavioral judgments and deep neural networks categorized images mainly by animacy, setting all objects (lookalike and inanimate) apart from the animate ones. In contrast, activity patterns in ventral occipitotemporal cortex (VTC) were better explained by object appearance: animals and lookalikes were similarly represented and separated from the inanimate objects. Furthermore, the appearance of an object interfered with proper object identification, such as failing to signal that a cow mug is a mug. The preference in VTC to represent a lookalike as animate was even present when participants performed a task requiring them to report the lookalikes as inanimate. In conclusion, VTC representations, in contrast to neural networks, fail to represent objects when visual appearance is dissociated from animacy, probably due to a preferred processing of visual features typical of animate objects. How does the brain represent objects that we perceive around us? Recent advances in artificial intelligence have suggested that object categorization and its neural correlates have now been approximated by neural networks. Here, we show that neural networks can predict animacy according to human behavior but do not explain visual cortex representations. In ventral occipitotemporal cortex, neural activity patterns were strongly biased toward object appearance, to the extent that objects with visual features resembling animals were represented closely to real animals and separated from other objects from the same category. This organization that privileges animals and their features over objects might be the result of learning history and evolutionary constraints.
Topics: Adult; Brain Mapping; Female; Humans; Magnetic Resonance Imaging; Male; Neural Networks, Computer; Pattern Recognition, Visual; Visual Cortex; Visual Pathways
PubMed: 31196934
DOI: 10.1523/JNEUROSCI.1714-18.2019