The authors would like to thank Autumn Alexander, Jeff Killen, Charles Wells, Kathy Pearson, and Vaibhav Paneri for their help with the project at different stages. This work is supported by the UAB Department of Psychology Faculty startup funds, the McNulty-Civitan Scientist Award&amp; the CCTS Pilot Research Grant (5UL1RR025777) to RK.

1. Special Techniques for Scanning Individuals with Developmental Disabilities:
While neuroimaging itself is a complex technique, using MRI to scan the pediatric population and people with developmental disorders can be extremely challenging.The main problems are: 1) Head motion: people with disorders, especially children, may find it difficult to keep still in the fMRI scanner throughout a scanning session. This might result in head motion which in turn may affect the quality of the data; 2) Children with autism have extreme sensory sensitivities and may be bothered by factors, such as scanner noise, being in closed space, temperature and so on; and 3) Anxiety and getting adjusted to a new environment can be difficult for people with autism. A change in their routine can pose problems if not prepared well. Therefore, innovative procedures with careful preparations are needed to achieve good yield, and to improve the quality of the data collected. We incorporate valuable insights gained from theory and practice to prepare a participant for an MRI scan, to make the experiment and scanning process enjoyable for the participant, and to process the collected data, some of which are:
<ol>
<li>Social Stories. Social stories are short, direct stories often used for explaining novel and confusing situations to children with autism8. We use social stories, written from the perspective of the individual with autism, to illustrate and verbally describe each step of our study process. At each item in the story, both verbal and pictorial descriptionsare provided. Titled "about my MRI session", we provide the story to the participant ahead of their scan day so that they can become familiar with the scanning process. The goal of the story is to increase the individual's understanding of the procedure, and to make him/her more comfortable in a new situation.</li>
<li>CD Recording of Scanner Sounds. During a scanning session, the MRI scanner produces loud noises constantly and this may be aversive to some individuals with autism. In order to acclimatize the participantsto the scanner noise, we send the participants (prior to the scan day) a recording of the sounds made by the scanner.</li>
<li>Mock MRI Scanner. We simulate an MRI scanning session with the participant using a mock scanner, constructed out of a discarded Phillips MRI scanner. This provides a realistic approximation of the actual scanning session. Use of this mock scanner, located at the Department of Optometry, UAB, allows the participant to become accustomed to the scanner environment.</li>
<li>Tour of the MRI Scanner Prior to Scanning. Prior to beginning the MRI scan, the participant is provided with an opportunity to see the scanner and even get on the scanner bed briefly. Usually, this helps to alleviate fear and anxiety, as well as provide the researchers with behavioral information regarding the participant's reaction to the scanner. Such reactions often provide valuable, though intuitive and qualitative, information of whether the participant may likely complete the whole scan.Before the participant goes into the scanner, he/she leaves all his belongings in a locker room and is also checked for metal using a metal detector. </li>
<li>Making the MRI Scanner child-friendly. For all our scans, we use the Siemens 3.0 Tesla Allegra MRI Scanner located at the UAB Civitan International Research Center. This is a head-only scanner making it less intimidating for participants. In order to make the scanner environment as child-friendly as possible (for the pediatric population), the scanner may be decorated with easily removable stickers of animals, cartoon characters, etc. In addition, we provide colorful blankets to the participants to keep them warm in the scanner. For children with autism who often have special interests (e.g., trains), such interests may be taken into account while decorating the scanner.</li>
<li>Use of Movies or Cartoons: The anatomical and DTI image acquisition do not require the participant to perform a task in the scanner. During these scans, participants are given the option of watching a few minutes of their favorite movie or cartoon series. In addition to providing a welcome break from the tasks, this helps make the scanning process more enjoyable for the participant.</li>
</ol>
2. Use of Stimulus Presentation Software and Button Response Devices to Communicate with the Scanner:
<ol>
<li>The experimental tasks are programmed using E-Prime (Psychology Software Tools, Pittsburgh, PA) stimulus presentation software. Before the scanning session, the participant practices shorter versions of the tasks on a laptop computer so that they are familiar with what they will see in the scanner and what buttons they will be required to press.</li>
<li>The tasks are loaded onto the Integrated Functional Imaging System (IFIS, Invivo Corporation, Orlando, FL), and are synchronized with the scanning paradigm. The IFIS system helps project the visual stimuli onto a screen behind the participant while in the scanner, which the participant views through a mirror attached to the head coil.</li>
<li>Dual monitors in the control room allow the researchers to select the experimental tasks or movies presented during the scan, and monitor participant responses (including response time and performance accuracy).</li>
<li>The participants wear MRI compatible headphones that allow them to hear audio, listen to the researchers' instructions, as well as reduce the obtrusive noise of the scanner. In addition to the headphones, earplugs are provided to further reduce the noise of the scanner.</li>
<li>A fiber optic button response device attached to each hand allows the participant to respond to task questions. The IFIS system records these responses, as well as the timing of each response in conjunction with the scan timing.</li>
<li>An emergency "squeeze ball" is given to the participant in case he/she does not want to continue the scan. Pressing this ball will set off an alarm in the control room prompting the researchers to get to the participant immediately. </li>
</ol>
3. Use of Static and Dynamic Visual Stimuli to Elicit Brain Responses in Participants with Autism:
While an excellent experimental design is critical to any scientific study, striking a chord with the participants can have a significant impact on the data acquired, especially in neuroimaging. The stimuli should be at the level of comprehension of the participant, and the experiment should be short, precise, and enjoyable. If adequate attention is not given to these elements, the quality of the data can be negatively affected. Special care is taken to try to make the experimental tasks challenging and enjoyable by creating innovative stimuli.
<ol>
<li>Dynamic visual stimuli, such as videos depicting social interaction are used to elicit participant responses on mental state attribution. In addition to being short and enjoyable, these stimuli are slices of the real social world and provide an appropriate arena for investigating the brain responses associated with social cognition.</li>
<li>Static visual stimuli, such as stick figure characters displaying different body postures are also used to study social cognition. These stimuli are helpful in studying emotions by encouraging the participants to infer feelings from body language.</li>
<li>Static visual stimuli like comic strip vignettes that involve multiple characters depicting social situations are also used. These stimuli involve attributions based on folk physics and folk psychology.</li>
<li>For studies examining language processing, we mainly use tasks that involve sentence comprehension, lexical decision-making, and discourse processing.</li>
<li>Although the length of each experiment differs from another, we try to keep every experiment less than 10 minutes. In addition, we also try to sandwich our DTI scan and Anatomical scans in between experiments to give the participant some free/resting time. We found reasonable success with this strategy. In one scanning session, we try to include 2-3 tasks taking the total time spent in the magnet to about 30-40 minutes. See Figure 1 for a flow-chart depicting the study protocol.</li>
</ol>
4. Data Acquisition, Storage, Analysis, &amp; Quality Control: 
Data Acquisition:
<ol>
<li>Functional MRI and DTI data are collected in a single session per participant using a Siemens 3.0 Tesla Allegra head-only Scanner (Siemens Medical Inc., Erlangen, Germany) housed in the Civitan International Research Center, University of Alabama at Birmingham. </li>
<li>The scanning session starts with high resolution T1-weighted scans for structural imaging. These are acquired using a 160-slice 3D MPRAGE (Magnetization Prepared Rapid Gradient Echo) volume scan with TR (Repetition Time) = 200 ms, TE (Echo Time) = 3.34 ms, flip angle = 12 degree, FOV (Field of View) = 25.6 cm, 256 X 256 matrix size, and 1 mm slice thickness. This acquisition lasts approximately 8 minutes and the data acquired provide anatomical information about each participant's brain.</li>
<li>The anatomical scans are followed by functional scans. To acquire functional images, we use a single-shot gradient-recalled echo-planar pulse sequence with TR= 1000 ms, TE = 30ms, flip angle = 60 degrees, FOV = 24 cm, and matrix =64 x 64. We acquire seventeen adjacent oblique axial slices in an interleaved sequence with 5 mm slice thickness, 1 mm slice gap, a 24 cm FOV, and a 64 X 64 matrix, resulting in an in-plane resolution of 3.75 X 3.75 X 5 mm.</li>
<li>Depending on the length of a functional MRI experiment, two or three experiments are included in a 60-75 minutes scanning session.</li>
<li>The DTI images are acquired using a single-shot, spin-echo, EPI (Echoplanar Imaging) sequence with 46 orthogonal directions. A diffusion weighted, single-shot, spin-echo, echo-planar imaging sequence is used with TR = 7000 ms, TE = 90 ms, bandwidth = 2790 Hz/voxel, FOV = 220mm, and matrix size = 128x 128. Twenty-seven 3-mm thick slices are imaged (no slice gap) with no diffusion-weighting (b = 0s/mm2) and with diffusion-weighting (b = 1000s/mm2) gradients applied in 46 orthogonal directions.</li>
</ol>
Data Storage and Data Analysis: 
<ol>
<li>The acquired neuroimaging data from an MRI session are transferred to a pass wall protected computer network at the University Hospital in line with the Health Insurance Portability and Accountability Act (HIPAA).</li>
<li>The MRI and DTI data from this server are transferred to the lab's centralized computer server (neuron), and anonymized before it is made available for data analyses. The neuron server houses all the image analysis programs, as well as the in-house scripts generated to do the computations specific to our experiments.</li>
<li>The computer cluster employs 3 nodes, each with a quad-core processor, enabling faster and parallel processing of multiple datasets. In addition, since the data from different studies reside at a common location, it makes it easier to organize the data for meta-analyses and to make overarching inferences.</li>
<li>The fMRI data are pre- and post-processed, and statistically analyzed using SPM8 (Statistical Parametric Mapping; Wellcome Department of Cognitive Neurology, London, UK). In addition, other software programs, such as Analysis of Functional NeuroImages (AFNI), fMRIB Software Library (FSL), and MRICron are also used for other analyses.</li>
<li>The DTI images are pre- and post-processed, and statistically analyzed using FSL.</li>
</ol>
Quality Control:
<ol>
<li>Temporal and spatial adjustments are done to the fMRI data using preprocessing steps, such as slice timing correction, motion correction, realignment, spatial normalization, and spatial smoothing.</li>
<li>Signal to noise ratio (SNR) is calculated by taking the ratio between the task-related variability and the non-task related variability. Noise (non-task related variability) can include anything from thermal noise to head motion effects. By both calculating the SNR to gain a relatively higher ratio (&gt; .8) and by controlling for artifacts, we can make sure that the images meet strict quality standards.</li>
<li>Temporal signal to noise ratio (tSNR) is the SNR over the entire course of the experiment and is mathematically defined by the ratio of mean signal intensity to the variation of the signal over time. The mean and standard deviation are taken at each voxel and if the ratio within the brain is at an acceptable threshold, the images can be used for further analyses.</li>
<li>It is always a good idea to examine the data for artifacts at every preprocessing and analysis step. For example, examining the raw images for Radio Frequency(RF) artifacts or assessing motion artifacts in the preprocessed data. One preventative measure for controlling for artifacts is to screen subjects for metal in or around the head, such as braces or a permanent retainer, to limit the amount of signal drop out.</li>
<li>If a dataset has too much noise, even after motion correction procedures, and does not meet our data quality standards, that dataset is usually excluded from further analyses.</li>
</ol>
5. Examining the Brain in Autism at a Network Level: fMRI-based Investigation of Functional Connectivity and DTI-based Examination of Anatomical Connectivity:
Functional Connectivity:
Functional connectivity refers to the synchronization of brain activation across different regions in the brain. The correlation of the time course of activation across brain areas is taken as evidence of the communication or connectivity between those regions. The steps involved in this analysis are as follows:
<ol>
<li>Regions of interest (ROIs) are identified, either functionally (based on activation response to tasks) or anatomically (based on standardized brain atlases). These ROIs are defined either spherically with a radius that would encompass the activation OR they are defined in their original shape. </li>
<li>The specified radius or actual shape, along with the MNI coordinates, is incorporated to create an ROI file for all ROIs using an in-house script.The presence of overlap among the locations of these ROIs is investigated and corrected. </li>
<li>For each ROI, the signal is extracted from the time course of the experiment from each individual participant's data.</li>
<li>For each participant, the average signal time course for each ROI is correlated with all other ROIs resulting in a correlation matrix. The correlation values are then converted to Fisher's z' scores for further statistical analyses to make individual, group, and between group level inferences.</li>
</ol>
Anatomical Connectivity (DTI):
In order to examine the white matter integrity across the brain, the diffusion tensor images are analyzed using fMRIB Software Library (FSL)9. Below are the main steps involved: 
<ol>
<li>The first step in this analysis involves preprocessing, including skull stripping and eddy current correction. Skull stripping is done using Brain Extraction Tool (BET) to remove any non-parenchymal tissue. When high intensity diffusion gradients are rapidly switched, shear and stretch artifacts are produced which are different for each gradient direction. These distortions are corrected using FSL's eddy current correction which registers the diffusion images to a reference image with no applied diffusion gradient.</li>
<li>Diffusion tensors and fractional anisotropy (FA) values, an index of water diffusion along axons, are then calculated at the voxel level by using FSL's Diffusion Toolbox.</li>
<li>Group differences on a voxel-by-voxel level are examined using Tract-Based Spatial Statistics (TBSS)10. In this technique, all diffusion images are first aligned into a common space using nonlinear registration.</li>
<li>A FA skeleton of all the major white matter tracts from all participants is created. Individual diffusion images of all participants are then registered to this FA tract skeleton.</li>
<li>Areas along this skeleton from the images of participants with autism are compared voxel-by-voxel to the same areas from the control participants using t-tests. Voxels with varying FA values are then isolated as a large ROI and the mean FA values calculated.</li>
</ol>
6. Representative Results:
The primary results emerging from our studies pertain to weakened neural response in participants with autism (in terms of activation, change in signal intensity, and in functional connectivity) and the possible use of altered cortical route in accomplishing cognitive and social tasks. For instance, the core regions found to be mediating a function (for e.g. posterior superior temporal sulcus at the temporoparietal junction in inferring intentions of others; see Figure 2) seem to under-respond in autism, relative to typical control participants. In addition, the core region seems underconnected functionally with other nodes, especially the spatially distant ones (figure 3). With DTI, we also find some anatomical basis to these findings (see Figure 4), providing a comprehensive, network-level picture of brain organization in autism. 
<img src="/files/ftp_upload/3178/3178fig1.jpg" alt="Figure 1"/> Figure 1. Flow-chart depicting the methods and procedures.
<img src="/files/ftp_upload/3178/3178fig2.jpg" alt="Figure 2"/> Figure 2: A) Increased activation in a typical language task, such as sentence comprehension (left inferior frontal gyrus, and left posterior superior temporal sulcus); B) Increased bilateral posterior superior temporal sulci activation in neurotypical participants during attribution of mental states to others (FWE corrected threshold of p&lt; 0.05).
<img src="/files/ftp_upload/3178/3178fig3.jpg" alt="Figure 3"/> Figure 3. Significantly reduced functional connectivity (synchronization of brain activation) between frontal and temporal regions in a social cognition task in participants with autism (p&lt; 0.05). LSTG: left superior temporal gyrus, RSTG: right superior temporal gyrus, RIFG: right inferior frontal gyrus, ROI: Region of Interest, FCA: functional connectivity. 
<img src="/files/ftp_upload/3178/3178fig4.jpg" alt="Figure 4"/> Figure 4. DTI Tractography results showing a white matter fiber bundle proceeding from the temporal lobe to the temporoparietal junction. The initial starting point for tractography was an ROI identified by TBSS as having a significantly smaller FA value in young adults with autism when compared to age matched typical control participants.

<ol>
	<li>Friston, K. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Functional+and+effective+connectivity+in+neuroimaging:+A+synthesis.">Functional and effective connectivity in neuroimaging: A synthesis.</a> Human Brain Mapping. 2, 56-78 (1994).</li><li>Just, M. A., Cherkassky, V. L., Keller, T. A., Minshew, N. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cortical+activation+and+synchronization+during+sentence+comprehension+in+high-functioning+autism:+evidence+of+underconnectivity.">Cortical activation and synchronization during sentence comprehension in high-functioning autism: evidence of underconnectivity.</a> Brain: a journal of neurology. 127, 1811-1821 (2004).</li><li>Kana, R. K., Keller, T. A., Cherkassky, V. A., Minshew, N. J., Just, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sentence+comprehension+in+autism:+thinking+in+pictures+with+decreased+functional+connectivity.">Sentence comprehension in autism: thinking in pictures with decreased functional connectivity.</a> Brain: a journal of neurology. 129, 2484-2493 (2006).</li><li>Koshino, H., Kana, R. K., Keller, T. A., Cherkassky, V. L., Minshew, N. J., Just, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=fMRI+Investigation+of+Working+Memory+for+Faces+in+Autism:+Visual+Coding+and+Underconnectivity+with+Frontal+Areas.">fMRI Investigation of Working Memory for Faces in Autism: Visual Coding and Underconnectivity with Frontal Areas.</a> Cerebral Cortex. 18, 289-300 (2007).</li><li>Kana, R. K., Keller, T. A., Minshew, N. J., Just, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Inhibitory+control+in+high-functioning+autism:+decreased+activation+and+underconnectivity+in+inhibition+networks.">Inhibitory control in high-functioning autism: decreased activation and underconnectivity in inhibition networks.</a> Biological Psychiatry. 62, 196-208 (2007).</li><li>Just, M. A., Cherkassky, V. L., Keller, T. A., Kana, R. K., Minshew, N. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Functional+and+Anatomical+Cortical+Underconnectivity+in+Autism:+Evidence+from+an+fMRI+Study+of+an+Executive+Function+Task+and+Corpus+Callosum+Morphometry.">Functional and Anatomical Cortical Underconnectivity in Autism: Evidence from an fMRI Study of an Executive Function Task and Corpus Callosum Morphometry.</a> Cerebral Cortex. 17, 951-961 (2007).</li><li>Castelli, F., Frith, C., Happe, F., Frith, U. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Autism,+Asperger+syndrome+and+brain+mechanisms+for+the+attribution+of+mental+states+to+animated+shapes.">Autism, Asperger syndrome and brain mechanisms for the attribution of mental states to animated shapes.</a> Brain. 125, 1839-1849 (2002).</li><li>Gray, C. A., Garand, J. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Social+stories:+Improving+responses+of+students+with+autism+with+accurate+social+information.">Social stories: Improving responses of students with autism with accurate social information.</a> Focus on Autistic Behavior. 8, 1-10 (1993).</li><li>Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Advances+in+functional+and+structural+MR+image+analysis+and+implementation+as+FSL.">Advances in functional and structural MR image analysis and implementation as FSL.</a> NeuroImage. 23, 208-219 (2004).</li><li>Smith, S. M., Jenkinson, M., Johansen-Berg, H., Rueckert, D., Nichols, T. E., Mackay, C. E., Watkins, K. E., Ciccarelli, O., Cader, M. Z., Matthews, P. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tract-based+spatial+statistics:+Voxelwise+analysis+of+multi-subject+diffusion+data.">Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data.</a> NeuroImage. 31, 1487-1505 (2006).</li><li>Li, Q., Sun, J., Guo, L., Zang, Y., Feng, Z., Huang, X., Yang, H., Lv, Y., Huang, M., Gong, Q. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Increased+fractional+anisotropy+in+white+matter+of+the+right+frontal+region+in+children+with+attention-deficit/hyperactivity+disorder:+a+diffusion+tensor+imaging+study.">Increased fractional anisotropy in white matter of the right frontal region in children with attention-deficit/hyperactivity disorder: a diffusion tensor imaging study.</a> Neuro Endocrinol Lett. 31, 747-753 (2010).</li><li>Jeong, J. W., Sundaram, S. K., Kumar, A., Chugani, D. C., Chugani, H. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Aberrant+diffusion+and+geometric+properties+in+the+left+arcuate+fasciculus+of+developmentally+delayed+children:+a+diffusion+tensor+imaging+study.">Aberrant diffusion and geometric properties in the left arcuate fasciculus of developmentally delayed children: a diffusion tensor imaging study.</a> AJNR Am J Neuroradiol. 32, 323-330 (2011).</li><li>Mulder, M. J., van Belle, J., van Engeland, H., Durston, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Functional+connectivity+between+cognitive+control+regions+is+sensitive+to+familial+risk+for+ADHD.">Functional connectivity between cognitive control regions is sensitive to familial risk for ADHD.</a> Human Brain Mapping. , (2010).</li><li>Vourkas, M., Micheloyanni, S., Simos, P. G., Rezaie, R., Fletcher, J. M., Cirino, P. T., Papanicolaou, A. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dynamic+task-specific+brain+network+connectivity+in+children+with+severe+reading+difficulties.">Dynamic task-specific brain network connectivity in children with severe reading difficulties.</a> Neurosci Lett. 488, 123-128 (2011).</li></ol>

No conflicts of interest declared.

The methods and procedures described in this paper are grounded in basic principles of cognitive neuroscience and neuroimaging. Taken together, these methods provide a compelling framework for assessing the brain functioning at the systems level in children, adults, and in people with disorders. Studies grounded in these methods have been especially influential in characterizing the discordant brain functioning in individuals with autism. 
Although the techniques presented here are transferrab...

probing the brain in autism using fmri and diffusion tensor imaging

Newly emerging theories suggest that the brain does not function as a cohesive unit in autism, and this discordance is reflected in the behavioral symptoms displayed by individuals with autism. While structural neuroimaging findings have provided some insights into brain abnormalities in autism, the consistency of such findings is questionable. Functional neuroimaging, on the other hand, has been more fruitful in this regard because autism is a disorder of dynamic processing and allows examination of communication between cortical networks, which appears to be where the underlying problem occurs in autism. Functional connectivity is defined as the temporal correlation of spatially separate neurological events1. Findings from a number of recent fMRI studies have supported the idea that there is weaker coordination between different parts of the brain that should be working together to accomplish complex social or language problems2,3,4,5,6. One of the mysteries of autism is the coexistence of deficits in several domains along with relatively intact, sometimes enhanced, abilities. Such complex manifestation of autism calls for a global and comprehensive examination of the disorder at the neural level. A compelling recent account of the brain functioning in autism, the cortical underconnectivity theory,2,7 provides an integrating framework for the neurobiological bases of autism. The cortical underconnectivity theory of autism suggests that any language, social, or psychological function that is dependent on the integration of multiple brain regions is susceptible to disruption as the processing demand increases. In autism, the underfunctioning of integrative circuitry in the brain may cause widespread underconnectivity. In other words, people with autism may interpret information in a piecemeal fashion at the expense of the whole. Since cortical underconnectivity among brain regions, especially the frontal cortex and more posterior areas 3,6, has now been relatively well established, we can begin to further understand brain connectivity as a critical component of autism symptomatology.
A logical next step in this direction is to examine the anatomical connections that may mediate the functional connections mentioned above. Diffusion Tensor Imaging (DTI) is a relatively novel neuroimaging technique that helps probe the diffusion of water in the brain to infer the integrity of white matter fibers. In this technique, water diffusion in the brain is examined in several directions using diffusion gradients. While functional connectivity provides information about the synchronization of brain activation across different brain areas during a task or during rest, DTI helps in understanding the underlying axonal organization which may facilitate the cross-talk among brain areas. This paper will describe these techniques as valuable tools in understanding the brain in autism and the challenges involved in this line of research.

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Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Neuroimaging techniques, such as functional MRI and Diffusion Tensor Imaging have become increasingly useful in characterizing the cognitive and neural deficits in autism. An examination of brain connectivity in autism at a network level along with adaptations for scanning children with developmental disabilities is presented.

Newly emerging theories suggest that the brain does not function as a cohesive unit in autism, and this discordance is reflected in the behavioral ...

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25.0K Views.  University of Alabama at Birmingham. Сканирование мозга методы, такие как функциональная МРТ и тензора диффузии изображений становятся все более полезными для характеристики когнитивных и нейронных дефицита при аутизме. Обследование головного мозга при аутизме связи на сетевом уровне, а также...

Video: Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking

Purpose: An accurate and practical method to measure parameters like strain in myocardial tissue is of great clinical value, since it has been shown, that strain is a more sensitive and earlier marker for contractile dysfunction than the frequently used parameter EF. Current technologies for CMR are time consuming and difficult to implement in clinical practice. Feature tracking is a technology that can lead to more automization and robustness of quantitative analysis of medical images with less time consumption than comparable methods.
Methods: An automatic or manual input in a single phase serves as an initialization from which the system starts to track the displacement of individual patterns representing anatomical structures over time. The specialty of this method is that the images do not need to be manipulated in any way beforehand like e.g. tagging of CMR images.
Results: The method is very well suited for tracking muscular tissue and with this allowing quantitative elaboration of myocardium and also blood flow.
Conclusions: This new method offers a robust and time saving procedure to quantify myocardial tissue and blood with displacement, velocity and deformation parameters on regular sequences of CMR imaging. It therefore can be implemented in clinical practice.

An accurate and practical method to measure parameters like strain in myocardial tissue is of great clinical value, since it has been shown, that strain is a more sensitive and earlier marker for contractile dysfunction than the frequently used parameter EF.

Purpose: An accurate and practical method to measure parameters like strain in myocardial tissue is of great clinical value, since it has been shown, that ...

A Strategy to Identify de Novo Mutations in Common Disorders such as Autism and Schizophrenia

There are several lines of evidence supporting the role of de novo mutations as a mechanism for common disorders, such as autism and schizophrenia. First, the de novo mutation rate in humans is relatively high, so new mutations are generated at a high frequency in the population. However, de novo mutations have not been reported in most common diseases. Mutations in genes leading to severe diseases where there is a strong negative selection against the phenotype, such as lethality in embryonic stages or reduced reproductive fitness, will not be transmitted to multiple family members, and therefore will not be detected by linkage gene mapping or association studies. The observation of very high concordance in monozygotic twins and very low concordance in dizygotic twins also strongly supports the hypothesis that a significant fraction of cases may result from new mutations. Such is the case for diseases such as autism and schizophrenia. Second, despite reduced reproductive fitness1 and extremely variable environmental factors, the incidence of some diseases is maintained worldwide at a relatively high and constant rate. This is the case for autism and schizophrenia, with an incidence of approximately 1% worldwide. Mutational load can be thought of as a balance between selection for or against a deleterious mutation and its production by de novo mutation. Lower rates of reproduction constitute a negative selection factor that should reduce the number of mutant alleles in the population, ultimately leading to decreased disease prevalence. These selective pressures tend to be of different intensity in different environments. Nonetheless, these severe mental disorders have been maintained at a constant relatively high prevalence in the worldwide population across a wide range of cultures and countries despite a strong negative selection against them2. This is not what one would predict in diseases with reduced reproductive fitness, unless there was a high new mutation rate. Finally, the effects of paternal age: there is a significantly increased risk of the disease with increasing paternal age, which could result from the age related increase in paternal de novo mutations. This is the case for autism and schizophrenia3. The male-to-female ratio of mutation rate is estimated at about 4&#x2013;6:1, presumably due to a higher number of germ-cell divisions with age in males. Therefore, one would predict that de novo mutations would more frequently come from males, particularly older males4. A high rate of new mutations may in part explain why genetic studies have so far failed to identify many genes predisposing to complexes diseases genes, such as autism and schizophrenia, and why diseases have been identified for a mere 3% of genes in the human genome. Identification for de novo mutations as a cause of a disease requires a targeted molecular approach, which includes studying parents and affected subjects. The process for determining if the genetic basis of a disease may result in part from de novo mutations and the molecular approach to establish this link will be illustrated, using autism and schizophrenia as examples.

Molecular genetic strategy for finding de novo mutations causing common disorders such as autism and schizophrenia.

There are several lines of evidence supporting the role of de novo mutations as a mechanism for common disorders, such as autism and schizophrenia. First, ...

MRI-guided Disruption of the Blood-brain Barrier using Transcranial Focused Ultrasound in a Rat Model

Focused ultrasound (FUS) disruption of the blood-brain barrier (BBB) is an increasingly investigated technique for circumventing the BBB1-5. The BBB is a significant obstacle to pharmaceutical treatments of brain disorders as it limits the passage of molecules from the vasculature into the brain tissue to molecules less than approximately 500 Da in size6. FUS induced BBB disruption (BBBD) is temporary and reversible4 and has an advantage over chemical means of inducing BBBD by being highly localized. FUS induced BBBD provides a means for investigating the effects of a wide range of therapeutic agents on the brain, which would not otherwise be deliverable to the tissue in sufficient concentration. While a wide range of ultrasound parameters have proven successful at disrupting the BBB2,5,7, there are several critical steps in the experimental procedure to ensure successful disruption with accurate targeting. This protocol outlines how to achieve MRI-guided FUS induced BBBD in a rat model, with a focus on the critical animal preparation and microbubble handling steps of the experiment.

Microbubble-mediated focused ultrasound disruption of the blood-brain barrier (BBB) is a promising technique for non-invasive targeted drug delivery in the brain1-3. This protocol outlines the experimental procedure for MRI-guided transcranial BBB disruption in a rat model.

Focused ultrasound (FUS) disruption of the blood-brain barrier (BBB) is an increasingly investigated technique for circumventing the BBB1-5. The BBB is a ...

Eye Tracking Young Children with Autism

The rise of accessible commercial eye-tracking systems has fueled a rapid increase in their use in psychological and psychiatric research. By providing a direct, detailed and objective measure of gaze behavior, eye-tracking has become a valuable tool for examining abnormal perceptual strategies in clinical populations and has been used to identify disorder-specific characteristics1, promote early identification2, and inform treatment3. In particular, investigators of autism spectrum disorders (ASD) have benefited from integrating eye-tracking into their research paradigms4-7. Eye-tracking has largely been used in these studies to reveal mechanisms underlying impaired task performance8 and abnormal brain functioning9, particularly during the processing of social information1,10-11. While older children and adults with ASD comprise the preponderance of research in this area, eye-tracking may be especially useful for studying young children with the disorder as it offers a non-invasive tool for assessing and quantifying early-emerging developmental abnormalities2,12-13. Implementing eye-tracking with young children with ASD, however, is associated with a number of unique challenges, including issues with compliant behavior resulting from specific task demands and disorder-related psychosocial considerations. In this protocol, we detail methodological considerations for optimizing research design, data acquisition and psychometric analysis while eye-tracking young children with ASD. The provided recommendations are also designed to be more broadly applicable for eye-tracking children with other developmental disabilities. By offering guidelines for best practices in these areas based upon lessons derived from our own work, we hope to help other investigators make sound research design and analysis choices while avoiding common pitfalls that can compromise data acquisition while eye-tracking young children with ASD or other developmental difficulties.

Eye tracking has long been used to study gaze patterns in typically-developing individuals, but recent technological advancements have made its use with clinical populations, including autism, more feasible. While eye-tracking young children with autism can offer insight into early symptom manifestations, it involves methodological challenges. Suggestions for best practices are provided.

The rise of accessible commercial eye-tracking systems has fueled a rapid increase in their use in psychological and psychiatric research. By providing a ...

The Use of Pharmacological-challenge fMRI in Pre-clinical Research: Application to the 5-HT System

Pharmacological MRI (phMRI) is a new and promising method to study the effects of substances on brain function that can ultimately be used to unravel underlying neurobiological mechanisms behind drug action and neurotransmitter-related disorders, such as depression and ADHD. Like most of the imaging methods (PET, SPECT, CT) it represents a progress in the investigation of brain disorders and the related function of neurotransmitter pathways in a non-invasive way with respect of the overall neuronal connectivity. Moreover it also provides the ideal tool for translation to clinical investigations. MRI, while still behind in molecular imaging strategies compared to PET and SPECT, has the great advantage to have a high spatial resolution and no need for the injection of a contrast-agent or radio-labeled molecules, thereby avoiding the repetitive exposure to ionizing radiations. Functional MRI (fMRI) is extensively used in research and clinical setting, where it is generally combined with a psycho-motor task. phMRI is an adaptation of fMRI enabling the investigation of a specific neurotransmitter system, such as serotonin (5-HT), under physiological or pathological conditions following activation via administration of a specific challenging drug.
The aim of the method described here is to assess brain 5-HT function in free-breathing animals. By challenging the 5-HT system while simultaneously acquiring functional MR images over time, the response of the brain to this challenge can be visualized. Several studies in animals have already demonstrated that drug-induced increases in extracellular levels of e.g. 5-HT (releasing agents, selective re-uptake blockers, etc) evoke region-specific changes in blood oxygenation level dependent (BOLD) MRI signals (signal due to a change of the oxygenated/deoxygenated hemoglobin levels occurring during brain activation through an increase of the blood supply to supply the oxygen and glucose to the demanding neurons) providing an index of neurotransmitter function. It has also been shown that these effects can be reversed by treatments that decrease 5-HT availability16,13,18,7. In adult rats, BOLD signal changes following acute SSRI administration have been described in several 5-HT related brain regions, i.e. cortical areas, hippocampus, hypothalamus and thalamus9,16,15. Stimulation of the 5-HT system and its response to this challenge can be thus used as a measure of its function in both animals and humans2,11.

The goal of this technique is to assess serotonin (5-HT) neurotransmitter function in the live and free-breathing animal with pharmacological magnetic resonance imaging (phMRI) and an intravenous challenge with a selective serotonin reuptake inhibitor (SSRI), fluoxetine.

Pharmacological MRI (phMRI) is a new and promising method to study the effects of substances on brain function that can ultimately be used to unravel ...

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion tensor imaging (DTI) techniques provide information on the microstructural processes of the cerebral white matter (WM) in vivo. The present applications are designed to investigate differences of WM involvement patterns in different brain diseases, especially neurodegenerative disorders, by use of different DTI analyses in comparison with matched controls. 	
DTI data analysis is performed in a variate fashion, i.e. voxelwise comparison of regional diffusion direction-based metrics such as fractional anisotropy (FA), together with fiber tracking (FT) accompanied by tractwise fractional anisotropy statistics (TFAS) at the group level in order to identify differences in FA along WM structures, aiming at the definition of regional patterns of WM alterations at the group level. Transformation into a stereotaxic standard space is a prerequisite for group studies and requires thorough data processing to preserve directional inter-dependencies. The present applications show optimized technical approaches for this preservation of quantitative and directional information during spatial normalization in data analyses at the group level. On this basis, FT techniques can be applied to group averaged data in order to quantify metrics information as defined by FT. Additionally, application of DTI methods, i.e. differences in FA-maps after stereotaxic alignment, in a longitudinal analysis at an individual subject basis reveal information about the progression of neurological disorders. Further quality improvement of DTI based results can be obtained during preprocessing by application of a controlled elimination of gradient directions with high noise levels.	
In summary, DTI is used to define a distinct WM pathoanatomy of different brain diseases by the combination of whole brain-based and tract-based DTI analysis.

Diffusion tensor imaging (DTI) basically serves as an MRI-based tool to identify in vivo the microstructure of the brain and pathological processes due to neurological disorders within the cerebral white matter. DTI-based analyses allow for application to brain diseases both at the group level and in single subject data.

Diffusion tensor imaging (DTI) techniques provide information on the microstructural processes of the cerebral white matter (WM) in vivo. The present ...

MRI and PET in Mouse Models of Myocardial Infarction

Myocardial infarction is one of the leading causes of death in the Western world. The similarity of the mouse heart to the human heart has made it an ideal model for testing novel therapeutic strategies.
In vivo magnetic resonance imaging (MRI) gives excellent views of the heart noninvasively with clear anatomical detail, which can be used for accurate functional assessment. Contrast agents can provide basic measures of tissue viability but these are nonspecific. Positron emission tomography (PET) is a complementary technique that is highly specific for molecular imaging, but lacks the anatomical detail of MRI. Used together, these techniques offer a sensitive, specific and quantitative tool for the assessment of the heart in disease and recovery following treatment.
In this paper we explain how these methods are carried out in mouse models of acute myocardial infarction. The procedures described here were designed for the assessment of putative protective drug treatments. We used MRI to measure systolic function and infarct size with late gadolinium enhancement, and PET with fluorodeoxyglucose (FDG) to assess metabolic function in the infarcted region. The paper focuses on practical aspects such as slice planning, accurate gating, drug delivery, segmentation of images, and multimodal coregistration. The methods presented here achieve good repeatability and accuracy maintaining a high throughput.

We describe how to perform MRI and PET imaging of the mouse heart. The protocol is tailored to assess treatment efficacy in models of myocardial infarction and heart failure.

Myocardial infarction is one of the leading causes of death in the Western world. The similarity of the mouse heart to the human heart has made it an ...

In Vivo Optical Imaging of Brain Tumors and Arthritis Using Fluorescent SapC-DOPS Nanovesicles

We describe a multi-angle rotational optical imaging (MAROI) system for in vivo monitoring of physiopathological processes labeled with a fluorescent marker. Mouse models (brain tumor and arthritis) were used to evaluate the usefulness of this method. Saposin C (SapC)-dioleoylphosphatidylserine (DOPS) nanovesicles tagged with CellVue Maroon (CVM) fluorophore were administered intravenously. Animals were then placed in the rotational holder (MARS) of the in vivo imaging system. Images were acquired in 10&#176; steps over 380&#176;. A rectangular&#160;region of interest (ROI) was placed across the full image width at the model disease site. Within the ROI, and for every image, mean fluorescence intensity was computed after background subtraction. In the mouse models studied, the labeled nanovesicles were taken up in both the orthotopic and transgenic brain tumors, and in the arthritic sites (toes and ankles). Curve analysis of the multi angle image ROIs determined the angle with the highest signal. Thus, the optimal angle for imaging each disease site was characterized. The MAROI method applied to imaging of fluorescent compounds is a noninvasive, economical, and precise tool for in vivo quantitative analysis of the disease states in the described mouse models.

In Vivo Optical Imaging of Brain Tumors and Arthritis Using Fluorescent SapC-DOPS Nanovesicles

We describe a multi-angle rotational optical imaging (MAROI) system for in vivo quantitation of a fluorescent marker delivered by saposin C (SapC)-dioleoylphosphatidylserine (DOPS) nanovesicles. Employing mouse models of cancer and arthritis, we demonstrate how the MAROI signal curve analysis can be used for the precise mapping and biological characterization of disease processes.

We describe a multi-angle rotational optical imaging (MAROI) system for in vivo monitoring of physiopathological processes labeled with a fluorescent ...

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Breast cancer is the most common cause of cancer among women worldwide. Early detection of breast cancer has a critical role in improving the quality of life and survival of breast cancer patients. In this paper a new approach for the detection of breast cancer is described, based on tracking the mammary architectural elements using diffusion tensor imaging (DTI). 
The paper focuses on the scanning protocols and image processing algorithms and software that were designed to fit the diffusion properties of the mammary fibroglandular tissue and its changes during malignant transformation. The final output yields pixel by pixel vector maps that track the architecture of the entire mammary ductal glandular trees and parametric maps of the diffusion tensor coefficients and anisotropy indices. 
The efficiency of the method to detect breast cancer was tested by scanning women volunteers including 68 patients with breast cancer confirmed by histopathology findings. Regions with cancer cells exhibited a marked reduction in the diffusion coefficients and in the maximal anisotropy index as compared to the normal breast tissue, providing an intrinsic contrast for delineating the boundaries of malignant growth. Overall, the sensitivity of the DTI parameters to detect breast cancer was found to be high, particularly in dense breasts, and comparable to the current standard breast MRI method that requires injection of a contrast agent. Thus, this method offers a completely non-invasive, safe and sensitive tool for breast cancer detection.

We describe how to obtain parametric and vector maps of the diffusion tensor of the breast using magnetic resonance imaging. The protocol and final output following imaging processing are tailored for tracking breast architectural features and detecting breast malignancy.

Breast cancer is the most common cause of cancer among women worldwide. Early detection of breast cancer has a critical role in improving the quality of ...

Using Saccadometry with Deep Brain Stimulation to Study Normal and Pathological Brain Function

The oculomotor system involves a large number of brain areas including parts of the basal ganglia, and various neurodegenerative diseases including Parkinson's and Huntington's can disrupt it. People with Parkinson's disease, for example, tend to have increased saccadic latencies. Consequently, the quantitative measurement of saccadic eye movements has received considerable attention as a potential biomarker for neurodegenerative conditions. A lot more can be learned about the brain in both health and disease by observing what happens to eye movements when the function of specific brain areas is perturbed. Deep brain stimulation is a surgical intervention used for the management of a range of neurological conditions including Parkinson's disease, in which stimulating electrodes are placed in specific brain areas including several sites in the basal ganglia. Eye movement measurements can then be made with the stimulator systems both off and on and the results compared. With suitable experimental design, this approach can be used to study the pathophysiology of the disease being treated, the mechanism by which DBS exerts it beneficial effects, and even aspects of normal neurophysiology.

This paper describes the use of quantitative measurement of eye movements in conjunction with stimulation of focal areas of the deep brain in order to study physiology, pathophysiology, and the mechanisms of deep brain stimulation.

The oculomotor system involves a large number of brain areas including parts of the basal ganglia, and various neurodegenerative diseases including ...