real time fMRI

Autism spectrum disorder (ASD) is a mental disorder and the main problem in ASD treatment has no definite cure, and one possible option is to control its symptoms. Conventional ASD assessment using questionnaires may not be accurate and required evaluation of trained experts. Several attempts to use resting-state functional magnetic resonance imaging (fMRI) as an assisting tool combined with a classifier have been reported for ASD detection. Still, researchers barely reach an accuracy of 70% for replicated models with independent datasets. Most of the ASD studies have used functional connectivity and structural measurements and ignored the temporal dynamics features of fMRI data analysis. This study aims to present several convolutional neural networks as tools for ASD detection based on temporal dynamic features classification and improve the ASD prediction results. The sample size is 82 subjects (41 ASD and 41 normal cases) collected from three different sites of Autism Brain Imaging Data Exchange (ABIDE). The default mode network (DMN) regions are selected for blood-oxygen-level-dependent (BOLD) signals extraction. The extracted BOLD signals' time-frequency components are converted to scalogram images and used as input for pre-trained convolutional neural networks for feature extraction such as GoogLenet, DenseNet201, ResNet18, and ResNet101. The extracted features are trained using two classifiers: support vector machine (SVM) and K-nearest neighbours (KNN). The best prediction results are 85.9% accuracy achieved by extracted the features from DenseNet201 network and classified these features by KNN classifier. Comparison with previous studies, has indicated the good potential of the proposed model for diagnosis of ASD cases. From another perspective, the presented method can be applied for analysis of rs-fMRI data on other type of brain disorders.

“What moves as a body, returns as the movement of thought.”
Of subjectivity (in its nascent state)
Of the social (in its mutant state)
Of the environment (at the point it can be reinvented)
“A process set up anywhere reverberates everywhere.”
The Technologies of Lived Abstraction book series is dedicated to work of trans- disciplinary reach, inquiring critically but especially creatively into processes of subjective, social, and ethical-political emergence abroad in the world today. Thought and body, abstract and concrete, local and global, individual and col- lective: the works presented are not content to rest with the habitual divisions. They explore how these facets come formatively, reverberatively together, if only to form the movement by which they come again to differ.
Possible paradigms are many: autonomization, relation; emergence, complexity, process; individuation, (auto)poiesis; direct perception, embodied perception, perception-as-action; speculative pragmatism, speculative realism, radical em- piricism; mediation, virtualization; ecology of practices, media ecology; tech- nicity; micropolitics, biopolitics, ontopower. Yet there will be a common aim: to catch new thought and action dawning, at a creative crossing. Technologies of Lived Abstraction orients to the creativity at this crossing, in virtue of which life everywhere can be considered germinally aesthetic, and the aesthetic anywhere already political.
“Concepts must be experienced. They are lived.”

Gustav Fechner, the German experimental psychologist, coined the term psychophysics in 1860, publishing the first mathematical equation to model human consciousness.  Fechner assumed that any future approaches to consciousness would include mathematical and physical underpinnings. In 1995, the cognitive scientist and philosopher David Chalmers coined the phrase "the hard problem" as being that of connecting consciousness with some physical substrate.  According to Chalmers, "The really hard problem of consciousness is the problem of experience. Most existing theories of consciousness either deny the phenomenon, explain something else, or elevate the problem to an eternal mystery."  As Chalmers points out, unfortunately, the modern scientific community has not yet produced a model of consciousness supported by mathematics and physics, thus no progress in Fecher's "psychophysics." 
However there is growing interest among the scientific community in string theory, the only branch of mathematical geometry that has successfully explained recent discoveries in high energy particle physics. The mathematical proof of string theory requires the existence of a ten dimensional universe.  But in trying to map consciousness, science continues to limit its data search within time and the three spatial dimensions, ignoring six of string theory's ten dimensions  Perhaps because mainstream science tacitly limits itself to four of the ten dimensions, progress has come to a dead end. Accordingly, the most widely accepted theory among neurophysiologists is that consciousness is somehow a "byproduct" of the electrical sparking of neurons, an accidental epiphenomenon of the activities of nerve-filled wet meat.
This paper considers data beyond the four dimensional constraints of the scientific establishment. It is my contention that many recorded accounts of philosophers, mystics, and shamans can and should be considered as data. My thesis is that these accounts express experience and observation within one or more of the additional dimensions predicted by string theory. Any advance in understanding consciousness must take into account not only the language and domains of mathematics, particle physics, and neurobiology, but also the experiential domains of philosophers and mystics. It is in regarding data from these multiple domains of inquiry that a new model of consciousness will emerge. In this paper I hypothesize a psychophysics of consciousness, woven of strands from mathematics, mysticism, and physics, as well as contemplative participatory practice, to address and solve "the hard problem."

One and multi dimensional raw data collections introduce noise and artifacts, which need to be recovered from degradations by an automated filtering system before, further machine analysis. The need for automating wide-ranged filtering applications necessitates the design of generic filtering architectures, together with the development of multidimensional and extensive convolution operators. Consequently, the aim of this thesis is to investigate the problem of automated construction of a generic parallel filtering system. Serving this goal, performance-efficient FPGA implementation architectures are developed to realize parallel one/multi-dimensional filtering algorithms. The proposed generic architectures provide a mechanism for fast FPGA prototyping of high performance computations to obtain efficiently implemented performance indices of area, speed, dynamic power, throughput and computation rates, as a complete package. These parallel filtering algorithms and their automated generic architectures tackle the major bottlenecks and limitations of existing multiprocessor systems in wordlength, input data segmentation, boundary conditions as well as inter-processor communications, in order to support high data throughput real-time applications of low-power architectures using a Xilinx Virtex-6 FPGA board.
For one-dimensional raw signal filtering case, mathematical model and architectural development of the generalized parallel 1-D filtering algorithms are presented using the 1-D block filtering method. Five generic architectures are implemented on a Virtex-6 ML605 board, evaluated and compared. A complete set of results on area, speed, power, throughput and computation rates are obtained and discussed as performance indices for the 1-D convolution architectures. A successful application of parallel 1-D cross-correlation is demonstrated.
For two dimensional greyscale/colour image processing cases, new parallel 2-D/3-D filtering algorithms are presented and mathematically modelled using input decimation and output image reconstruction by interpolation. Ten generic architectures are implemented on the Virtex-6 ML605 board, evaluated and compared. Key results on area, speed, power, throughput and computation rate are obtained and discussed as performance indices for the 2-D convolution architectures. 2-D image reconfigurable processors are developed and implemented using single, dual and quad MAC FIR units. 3-D Colour image processors are devised to act as 3-D colour filtering engines. A 2-D cross-correlator parallel engine is successfully developed as a parallel 2-D matched filtering algorithm for locating any MRI slice within a MRI data stack library. Twelve 3-D MRI filtering operators are plugged in and adapted to be suitable for biomedical imaging, including 3-D edge operators and 3-D noise smoothing operators.
Since three dimensional greyscale/colour volumetric image applications are computationally intensive, a new parallel 3-D/4-D filtering algorithm is presented and mathematically modelled using volumetric data image segmentation by decimation and output reconstruction by interpolation, after simultaneously and independently performing 3-D filtering. Eight generic architectures are developed and implemented on the Virtex-6 board, including 3-D spatial and FFT convolution architectures. Fourteen 3-D MRI filtering operators are plugged and adapted for this particular biomedical imaging application, including 3-D edge operators and 3-D noise smoothing operators. Three successful applications are presented in 4-D colour MRI (fMRI) filtering processors, k-space MRI volume data filter and 3-D cross-correlator.

Evidence from imaging and anatomical studies suggests that the midcingulate cortex (MCC) is a dynamic hub lying at the interface of affect and cognition. In particular, this neural system appears to integrate information about conflict and punishment in order to optimize behavior in the face of action-outcome uncertainty. In a series of meta-analyses, we show how recent human electrophysiological research provides compelling evidence that frontal-midline theta signals reflecting MCC activity are moderated by anxiety and predict adaptive behavioral adjustments. These findings underscore the importance of frontal theta activity to a broad spectrum of control operations. We argue that frontal-midline theta provides a neurophysiologically plausible mechanism for optimally adjusting behavior to uncertainty, a hallmark of situations that elicit anxiety and demand cognitive control. These observations compel a new perspective on the mechanisms guiding motivated learning and behavior and provide a framework for understanding the role of the MCC in temperament and psychopathology.

This commentary elaborates on Gray's conclusion that his neurophysiological model of consciousness might explain how consciousness arises from the brain, but does not address how consciousness evolved, affects behaviour or confers survival value. The commentary argues that such limitations apply to all neurophysiological or other third-person perspective models. To approach such questions the first-person nature of consciousness needs to be taken seriously in combination with third-person models of the brain.

Added notes for 2013 online version: Nearly 20 years after its original publication this commentary on Jeffrey Gray’s thoughtful BBS target article still has contemporary relevance as, within an exclusively third-person evolutionary paradigm the evolution and function of first-person consciousness continues to present difficulties. Those interested in these issues may also want to look at my more recent online papers that address this in more detail, particularly The evolution of consciousness (2012) and Can evolutionary theory explain the existence of consciousness? (2011)

In cognitive science, there is an ongoing debate about the architecture of the mind: does it consist of a number of mental “organs” each managing a different function in isolation, or is it more of general processor, adaptable to a wide range of tasks? One corner of this debate has centered on face processing. This is because face-perception is crucial to normal human functioning and some evidence shows that faces may be processed by the brain in a privileged way compared to other types of stimuli. For example, in EEG brain recordings, the N170 is a characteristic signal that occurs after a participant is exposed to an image of a face, but it is much less pronounced when other stimuli are shown. More than 15 years of research on the “N170 face effect” have yielded the standard view that the N170 is at the very least face-sensitive, and possibly even face-specific, that is, indexing modular processes tied exclusively to facial geometries. The specificity claim is clearly stronger, and hence subject to significant controversy; while the more conservative “sensitivity” claim had been regarded (until recently) as effectively settled. Nevertheless, Thierry and colleagues, in a contentious 2007 article, sought to undermine even this ‘conservative’ consensus: they argued that the apparent face-responsiveness of the N170 in prior research was due to systematic flaws in experimental design. Fiery debate has followed. In this review, we put the debate in its historical and philosophical context, and try to spell out some of the theoretical and logical assumptions that underlie the claims of the competing camps. We then show that the best available evidence counts, at least partially, against the Thierry et al. construal of the N170. Accordingly, it would be premature to abandon the “conservative” account of the N170, according to which it is—minimally—responsive to faces. We conclude by returning to the more controversial claim about face-specificity, and try to clarify what such a view would entail from a theoretical standpoint.

The smell is based upon the perception in visual expression of advertising message. Messages stimulate ‘smell perception’ of receivers by way of revealing smell images in their memory. Creating common memories and association of ideas make reference to receiver’s experiences and enable smell images to transform into ‘smell perception’. To get through persons’ smell images has primarily required carrying out an elaborate research on the smell within thesis, sectioning the subjects in such a manner that it might be suitable for the purposes of thesis, and expressing the flow of information in a certain order. In this thesis, this method has been followed by carrying out a systematic research with a view to reaching a conclusion which includes some opinions as well. Being composed of four sections, the first section is dedicated to giving information on the operation of the sense of smell and emphasizing ‘the importance of smell’. In this scope, the impacts of favorable and unfavorable smells on people are investigated within historical process including communicative aspects of such impacts. In this section, where physiological, chemical and communicative properties of the smell become prominent, a general introduction to the sense of smell is provided and information on its way of affecting and directing human life is given.
In second section, ‘psychology’ and ‘neurological’ impacts of smell memory are handled; texts of the writers who discussed the smell and memories in a literary sense
are examined; fMRI test on the sense of smell is conducted, and whether or not the visual triggers the sense of smell is analyzed. Under fMRI smell test, which was conducted based on persons’ mental behaviors directed to smell, the matter of thesis is solved.
In third section, information is given on the fact that the smell is an important
sense that might be used to affect consumers in visual expression of advertising message; psychological impacts of the sense of smell on consumer’s / receiver’s buying decision are investigated; those product advertisements containing or making reference to smell are analyzed – based on visual expression strategies, and persons’ ‘smell images’ are clearly described in the light of all information obtained.
In fourth section, where relevant applications are presented, a publicity poster
is designed for ‘Rexona’-branded deodorant. The poster, which is designed with the help of smell images that might trigger the persons’ sense of smell, serves as a suggestion as well.

Multi-dimensional raw data collections introduce noise and artefacts, which need to be recovered from degradations by an automated filtering system before, further machine analysis. The need for automating wide-ranged filtering applications necessitates the design of generic filtering architectures, together with the development of multidimensional and extensive convolution operators. Consequently, the aim of this book is to vet the problem of automated construction of a generic parallel filtering system. Serving this goal, performance-efficient FPGA implementation architectures are developed to realize parallel multi-dimensional filtering algorithms. These generic architectures provide a mechanism for fast FPGA prototyping of high performance computations to obtain efficiently implemented performance indices of area, speed, dynamic power, throughput and computation rates, as a complete package. These algorithms and their architectures tackle the major bottlenecks and limitations of existing multiprocessor systems in wordlength, boundary conditions as well as interprocessor communications, in order to support high data throughput real-time applications of low-power architectures. Sami Kadhim Hasan ArRammahi received the PhD degree in computer engineering from the University of Newcastle, United Kingdom, in 2013.He is currently a DSP Professor with the College of Information Engineering, Al-Nahrain University. He is carrying out his research on developing real time DSP systems in both algorithmic and architectural level.

In this work we present a new open source software package offering a unified framework for the real-time adaptation of fMRI stimulation procedures. The software provides a straightforward setup and highly flexible approach to adapt fMRI paradigms while the experiment is running. The general framework comprises the inclusion of parameters from subject's compliance, such as directing gaze to visually presented stimuli and physiological fluctuations, like blood pressure or pulse. Additionally, this approach yields possibilities to investigate complex scientific questions, for example the influence of EEG rhythms or fMRI signals results themselves. To prove the concept of this approach, we used our software in a usability example for an fMRI experiment where the presentation of emotional pictures was dependent on the subject's gaze position. This can have a significant impact on the results. So far, if this is taken into account during fMRI data analysis, it is commonly done by...

"The current study is to examine the effect of ultrasonication on the synthesis of forsterite (Mg2SiO4) powder. Ultrasonication and ball milling were performed using talc and magnesium oxide as starting precursors, followed by heat treatment to obtain forsterite. The X-ray diffraction results of the powders heat treated at 1200 °C showed that the 2 hours ultrasonication followed by 3 hours ball milling was beneficial in retaining the forsterite phase in the matrix. The results were similar to ball milling for 10 hours prior to heat treatment. The derived powders also exhibited very fine crystallite size in the range of 28 to 35 nm thus indicating the viability of using ultrasonication as part of the processing steps in the synthesizing of forsterite ceramics.

http://www.scientific.net/AMR.576.252